Find out how data analytics and business intelligence can benefit healthcare organizations.
The digital health market is forecasted to be over two hundred fifty-eight billion dollars by 2029. This staggering increase in data assists companies in coming up with more and more innovative ideas, improving the advancement of medical studies and the patient care. Since big data analytics in healthcare, there is also a consequent rise in medical accomplishments. The analytical software options are descriptive, diagnostic, predictive, and prescriptive.
Descriptive and diagnostic analytics make up the bedrock of any business intelligence (BI) strategy. Descriptive analytics is the starting point in BI. It answers the questions of what happened. Descriptive analytics identifies patterns and trends that point to key performance indicators (KPIs) and current goals. Diagnostic analytics compares data trends and finds their causative factors: how and why things turned out the way they did. When a healthcare organization understands this, it makes its BI actionable.
Healthcare organizations can use BI reports to make more reasonable business decisions for new strategies. These reports help monitor and optimize KPIs, the financial state of the company, the level of patient loyalty and satisfaction, etc. The info from insurance, providers, suppliers, and other healthcare companies is combined with BI tools. Healthcare processes and workflows are seamless, from optimizing the supply chain to minimizing claim denials.
When a healthcare company knows what happened in the past, it can use that data to predict the future.Â
Predictive analytics creates the most probable scenarios for the development of events. These scenarios can be both successful and undesirable. For effective forecasts, it’s also necessary for the analytics models to be able to find patterns.
A Belitsoft expert Dmitry Baraishuk says developers train models based on a library system. The Python programming language has one of the richest ecosystems of libraries (NumPy, pandas, seaborn, matplotlib, machine learning libraries, etc.).
Python software development services are not limited to model training. The first thing top-notch Python developers do is to cleanse the sets of client data. They study this data carefully and determine the features most strongly related to the predicted variable. The next step for developers is to split the dataset into test and training data. They need to evaluate the performance of the created model and only then deploy it in a real app.
Prescriptive analytics offers reports with targeted actions. The provider can implement a successful predictive analytics scenario or avoid an unfavorable one.
How can Data Analytics Be Useful for Healthcare?
Most healthcare industry stakeholders consider investments in analytics and insights as a priority strategy today. The implementation of unified business intelligence solutions and predictive analytics allows healthcare organizations to define a more personalized plan for the treatment and care of patients.
Early and Rapid Diagnosis of Diseases
Massachusetts General Hospital has implemented deep learning (DL) techniques to automate separating MRI scans of brain tumors. The AI ​​algorithms dealt with tumor delineation much faster than traditional methods. Based on these results, doctors were able to speed up patient treatment planning. The reason for the success is that artificial intelligence (AI) analyzes many images at a time and can do this much more accurately than if the provider checks the photos manually.
The Alan Turing Institute uses AI-powered analytics to identify potential mental health problems. Algorithms compare brain scans of patients who have sought medical attention because they suspect they may have dementia with scans of patients with this diagnosis. The analytics system can find patterns in the images that even experienced neurologists might miss when they review scans manually. In preclinical trials, the data analytics software was able to identify dementia from scans years before a patient showed symptoms of the disease. In this case, early diagnosis can slow the progression of the disease and significantly improve patient outcomes.
Streamline the Drug Refill Workflow
With the help of EHR-integrated analytics tools, doctors can monitor changes in the patient’s condition. In addition, the analytics system shows the provider the changes in the results of lab tests. A built-in reminder system notifies the provider when patients need to renew their medications. The doctor studies the reports and decides whether the prescribed drug is suitable for the patient or a new one needs to be prescribed. Also, in the analytics reports, the provider can find info about other medications a patient takes and how they combine with the prescribed treatment. If the drug is effective and works well together with other prescribed drugs, the doctor renews the prescription for it. The analytics process such requests in just a couple of minutes. It saves time for health personnel.
Efficient Redistribution of Resources Between Emergency and Primary Care
A healthcare organization can use analytics software to allocate patient flow efficiently. MemorialCare health system implemented analytics tools into its organization’s management system. It was able to reduce the burden on emergency departments. The algorithms identified two groups of patients. The first group included patients diagnosed with a certain disease recently. The second group included patients for whom the disease had become chronic.
The analytics system found that patients in the first group were more likely to seek emergency care than primary care. It put an overutilization of emergency departments. MemorialCare’s care management team communicates with patients in the first group weekly. They explain to people that their situation needs primary care, not an emergency, and help them get it.
Monitor the Patient’s Condition
AI-powered analytics solutions run on wearables and collect real-time data to detect deterioration in a patient’s state. Wearables have an early warning scale that the provider customizes for each patient. The sensor measures vital signs: blood saturation, pulse pressure, etc.
Also, analytics tools can combine this data with digital therapeutics that measure a patient’s mood and cognitive function. The app calculates a personal well-being score, which can change for each patient several times a day. Analytics algorithms compare this score with anonymous aggregated scores from other users. They provide the patient with a predictive warning when their mental well-being may get worse.
When a patient is warned, they can take any action they consider right. For example, this patient can change their behavior or seek help from healthcare services.
In Addition
EHR-integrated analytics solutions help identify data that may potentially contain errors. Researchers from Johns Hopkins University School of Medicine have developed a method for analyzing symptom-disease pairs to identify common diagnostic errors. Analytical algorithms compare the symptoms of patients who seek medical care with the symptoms of diseases that can be misdiagnosed because they have many atypical symptoms. For example, dizziness can be a sign of both a stroke and inflammation of the inner ear. This analytical method reduces the risk of diagnostic errors and helps doctors determine the right strategy for further treatment.
About The Author:
Dmitry Baraishuk is a partner and Chief Innovation Officer at the software development company Belitsoft (a Noventiq company) with 20 years of expertise in digital healthcare, custom e-learning software development, and Business Intelligence (BI) implementation.
