Five real-world applications of data analytics in healthcare
The saying “health is the greatest possession” does hold in the case of the healthcare industry. Recent statistics show that the global healthcare industry is growing at an exceptional rate and will continue growing due to the current COVID chaos. According to research, global healthcare spending will reach over $10 trillion by 2022.
With the surging need for healthcare, the industry is dealing with large datasets. A large volume of data gets generated every second, which needs analysis for valuable insights.
The healthcare industry generates about 30% of the world’s data volume. The data includes hospital records, patients’ medical records, examination results, etc. But all this data isn’t of any use until refined to make it easily understandable by people. Healthcare must apply advanced analytics techniques to convert this raw data into insights or to obtain answers. Here, data analytics comes into action.
What is data analytics?
Data analytics is a process used to analyze structured and unstructured data using different methods. The goal is to capture trends and dig for insights that are inevident. Many industries are using data analytics for business-critical decision-making. Although, the healthcare industry is one sector that can feel more impact of this powerful technology.
Use cases of data analytics in the healthcare industry
Data analytics can transform healthcare services in various areas, including cancer treatment, medicine development, disease prediction, and more. Application of the advanced analytics techniques and the voluminous data for analysis will help healthcare organizations to great lengths, including achieving insurance rate accuracy, administrative process automation, and AI-assisted diagnostics.
Let’s look at some of the real-world applications of data analytics in healthcare that demonstrate how advanced analytics can refine processes, upgrade patient care, and, most importantly, save lives.
Predictions for increased and improved staffing
One classic example of big data in healthcare is its use in predicting staff requirements at any given time. Shift managers in hospitals and clinics often face challenges in selecting the number of staff to be allocated for any period. Put in too many workers, their labor costs increase. Putting too few workers will result in poor customer service and might even prove fatal for patients.
The healthcare sector can solve this problem by using data from various sources and analyzing it to predict how many patients to expect in hospitals at different hours. Such patient forecasts help with planning resource allocation through data visualization and result in improved patient care.
Analyzing electronic health records (EHRs)
An everyday use case for data analytics in healthcare is electronic health records (EHRs). EHRs trace and record patients’ health data like information about existing health conditions and allergies that eliminate the need for unnecessary tests and expenses. If healthcare providers share patient data as they treat patients, they can avoid redundant tests and improve patient care.
Of course, EHR is a remarkable initiative. Most of the countries still find it challenging to implement them. According to research, the US is the only country with 94% of its hospitals successfully adopting EHR.
Using health data to prevent advanced risks and diseases
Healthcare can use data analytics to address the hospitalization risk for patients suffering from chronic health problems. Health workers can filter details like symptoms, medication, number of ER visits, and trends. This way, healthcare facilities can deliver correct preventative care and reduce patient readmissions.
Through this application, the healthcare industry can reduce spending on in-house patients and ensure room and resources are accessible for those in dire need.
Identifying and preventing fraud
Studies suggest that 93% of healthcare organizations have encountered a data breach. Any data breach could prove problematic for both patients and healthcare organizations. The Healthcare industry can use data analytics to avoid security threats by identifying fluctuations in network traffic or any suspicious activity that suggests a security breach.
Data analytics can help detect anomalies and prevent fraud while streamlining healthcare operations.
Driving medicinal development
Let’s look at one of the most important uses of data analytics in healthcare — the future of medicine. In healthcare, data analytics can help in developing new therapies and discovering new drugs. Health experts can refer to historical and real-time data, apply predictive and data visualization techniques to identify the success rate of trials and processes.
In addition, the healthcare industry can apply data analytics to analyze genetic information and predict reactionary outcomes in patients for making groundbreaking discoveries. Data analytics can help health experts make crucial decisions that can drive innovation, streamline processes, and ultimately save lives by taking appropriate actions in time.
Data analytics challenges in healthcare organizations
Although there are many data analytics applications in the healthcare industry, organizations face obstacles in its widespread implementation.
One of the biggest challenges healthcare organizations face is that medical data sits across multiple sources protected by states, hospitals, and administrative authorities. Combining these different data sources would require an entirely new infrastructure that allows all healthcare data providers to share patient information.
The other challenge is implementing business intelligence. Healthcare providers need to identify how other healthcare organizations leverage data analytics to provide optimum patient care and satisfaction.
Data security is yet another concern for most healthcare organizations, with constant hacking and security breaches that occur continuously. Here, an access-based analytics solution can help achieve secure multi-level access by providing specific privileges to the relevant users.
Finding a reliable data analytics solution
A powerful analytics solution would allow you to harness information from historical and real-time data. Xtract.io’s data analytics solution analyzes large volumes of data and converts it into customized reports and visualizations for quick understanding. The timely and accurate data will ensure correct decision-making in all areas of healthcare. Our robust and scalable analytics solution employs predictive analytics and ML to prepare and enrich data available in different formats.
If you are looking for an affordable and result-driven solution to get the correct answers to your business questions, book a demo and check out how it works. We would love to learn about your requirements and offer tailored solutions that fit your business needs.
Originally published at https://xtract.io/blog/five-real-world-applications-of-data-analytics-in-healthcare/on November 29, 2021. Check out our blog to read more articles and follow us on Twitter or Facebook.