As the name suggests, analytics is assessing relevant data to gain insights about something. Healthcare analytics is used to sift through large amounts of relevant data about the medical industry to reveal important insights that benefit all stakeholders.
Healthcare organizations often strive to achieve peak performance by minimizing overhead, optimizing management workflows, improving productivity, and serving patients better. But doing so requires information about existing practices to find room for improvement.
HIPAA compliant solutions for healthcare data analytics, or medical informatics, help clinicians and administrators identify the problems in existing procedures and correct them for better revenues and patient outcomes. When it comes to health care data analytics, there are five main types to consider
1. Descriptive Analytics
Descriptive analysis is part of the broader healthcare analytics solutions suite that describes what has happened. It uses data from the recent to distant past and sheds light on the things that have occurred or even things happening.
An excellent example of a descriptive approach to advanced healthcare analytics was when experts used the rate of positive test results over time to know how contagious the Covid-19 virus is. In other words, descriptive analytics in healthcare “describes” the events that have happened. Insight is garnered in terms of statistics and pie charts.
This is an important aspect of acquiring business intelligence and provides a clearer picture of events to the leadership. Every industry uses descriptive text analytics, and it has worked wonders for the healthcare industry. Descriptive medical analytics software has helped doctors understand patient health better and helped population health management experts understand public health in a more nuanced way. Healthcare providers who utilize this form of clinical analytics have been able to make changes to their treatment, which has improved the overall patient experience.
2. Diagnostic Analytics
If descriptive analytics tells us what has happened, diagnostic analytics for healthcare help us understand why they happened. In other words, it’s like finding out the why or “diagnosing” the cause of something, and it helps us make better sense of the results produced by descriptive analytics.
Let’s build on the previous example. If a descriptive approach to healthcare analytics can tell us how contagious a virus is, diagnostic healthcare IT analytics can reveal why. As a result, it allows healthcare management experts to set guidelines to prevent an infectious virus from spreading further.
In healthcare and other industries, a diagnostic approach to AI in analytics has helped businesses pinpoint the reason for good or bad results and take measures accordingly. Diagnostic healthcare analytics solutions help clinicians understand why a specific treatment might deliver certain outcomes and implement appropriate measures.
3. Predictive Analysis
As the name indicates, this assessment method predicts if and how something might happen. Predictive analytical tools in healthcare can process historical information and deduce what will happen in the future. Using predictive healthcare data analytics software, patients electronic health records can often reveal who is at a higher risk of certain diseases.
This type of analytics is often carried out using machine learning. Predictive healthcare data analytics platforms helped experts estimate the rate of spread of infection during the Covid-19 pandemic. They could do this by assessing existing data about the virus and how it spreads. Parameters such as population density, medical facilities, government rules, etc helped predict how many people could be infected in a certain period.
Predictive analysis is one of the most powerful tools used in the broader suite of healthcare analytics solutions. With the advent of wearable health technology, patients’ health data can be captured in real-time and stored on a secure cloud. Cloud computing in healthcare coupled with predictive healthcare analytics services could assess this data and identify early signs of chronic disease. Having the power to predict if a person might develop chronic disease helps clinicians leverage patient engagement systems to customize doctor-patient interaction for better medical outcomes.
4. Prescriptive Analytics
Based on the prediction from predictive analysis, a prescriptive healthcare data analytics solution prescribes the proper action to be taken. It empowers doctors with insights into the factors that cause the prediction. As a result, clinicians can stage early medical interventions to prevent deterioration in a patient’s case.
Predictive and prescriptive healthcare data analytics solutions help develop healthcare automation platforms to engender efficient medical records management. Prescriptive clinical analytics software also helps pharmacists and public health experts cash in on positive outcomes and ascertain the effectiveness of established best practices.
5. Discovery Analytics
This technique belongs to a class of advanced analytics for healthcare. Many experts agree that it represents the forefront of clinical analytics. Discovery healthcare analytics software assesses multiple data points to predict newer outcomes. Researchers often use this method to develop novel drugs and physicians to vary their approach to treatments.
Historical information, predictions, and data garnered from various parameters aid in the effective utilization of healthcare data analytics services. Integrated healthcare solutions that support seamless healthcare interoperability through smooth electronic data exchange are vital for discovery analytics. Advancements in computing have helped experts use discovery healthcare analytics domains to test the feasibility of outcomes before they happen. Doing so allows more room for trial and error, which ultimately benefits every stakeholder.
Healthcare analytics solutions have helped improve the productivity of medical staff, revealed valuable insights into clinical workflows, highlighted important patterns, and raised the bar for the industry as a whole. Data Analytics in healthcare has been integrated with practice management software to improve outcomes at ambulatory care settings and hospital systems to optimize operations. Just as it has propelled every industry to newer heights, data analytics in healthcare has bolstered clinical research, medical outcomes, and insurance workflows. It’s an indispensable tool for addressing some of the most pressing challenges in public health.