Find out the top benefits of data analytics in healthcare and how it is useful for medical organizations.
Data analytics in healthcare has undergone significant changes in recent years. The traditional reactive model of healthcare no longer meets modern realities. Predictive analytics and preventive care are steadily replacing it. Today, analytics tools that rely on historical and real-time data to predict probable outcomes are in high demand. Healthcare organizations also need analytics software to determine actions to achieve favorable forecast situations and avoid unfavorable scenarios. Prescriptive analytics copes with this.
What Are the Benefits of Healthcare Data Analytics for Clinicians?
Early Detection and Prevention of Diseases
Modern analytics algorithms based on artificial intelligence (AI) and machine learning (ML) can detect subtle anomalies and patterns in patient data. They alert clinicians that a patient has a potential health risk. Developers can use different programming languages ??to create healthcare analytics software. Python-based web development and applications are very popular today.
A Belitsoft expert Dmitry Baraishuk explains that this programming language is easy to understand and well-structured. When an application that was originally written in Python needs legacy app modernization, it is easier to do refactoring. Specialists from the Python app development company only need to update the easy-to-read Python code. Refactoring does not require them to rewrite the entire app. Python algorithms study the patients’ genetics and provide a report on why a specific person or focus group got sick. The same data allows the data analytics system to understand how genetics is associated with the risk that a patient will develop a specific disease.
Medication Management
When a provider prescribes a treatment, they can monitor how the patient takes medications using a medication delivery unit (MDU). The physician sets the dosage and frequency of medication administration. MDUs «dispense» medications according to these settings, maintain logs, and act as electronic medication administration records (eMARs). Analytics algorithms collect data from the MDU. In these reports, the physician sees how well the patient follows their orders. They can remotely adjust the treatment strategy and change the medication dosage if necessary.
What Are the Benefits of Data Analytics for Insurers?
Detect Provider Fraud
An unscrupulous healthcare provider may provide a patient with cheaper services than indicated on the bill. Also, such a provider may add a service to a patient’s bill that the patient didn’t receive. For example, an insurer receives a claim stating that the patient had an appointment with a doctor. In reality, this visit never took place. A third, but not the least, type of fraud is when a provider intentionally «upcodes» a patient’s diagnosis to bill for a higher-priced treatment.
Data analytics tools help prevent fraud. The fraud analytics algorithms «notice» if a particular provider performs a lot of claims for expensive procedures. The system allows insurance companies to analyze past claims to detect potentially fraudulent behavior. Suspicious claims can be marked for further investigation. It drives the legitimate claims process.
Reduce the Risk of Losing a Client
Healthcare data analytics and business intelligence tools monitor patient no-show rates for in-person appointments. Health centers can use dashboards with dynamic charts to analyze no-show rates and predict attendance. If a patient has missed many in-person visits, there is a risk that they will eventually want to cancel their insurance. Insurers can plan targeted interventions based on the predictive analytics report. For example, they can contact the patient and ask how they can help them keep up with their appointments. A strategy to remind the patient of upcoming appointments via phone or text message may be a good option. This way, the patient is satisfied that they remember their appointments and the insurer can reduce the risk of cancellations and minimize customer churn.
How Is Data Analytics Useful for Healthcare Organizations?
Supply Chain Management
Doctors and nurses spend a fifth of their time searching for the right medical supplies for a patient’s procedure. Instead, they could be doing their job and providing patients with medical or nursing care. When employees are forced to spend their time inefficiently, they affect the company’s revenue. To ensure that healthcare workers always have the right resources at their fingertips, a healthcare organization must organize an effective supply chain. It can be done in different ways. One option is to analyze patients’ EHRs. Data analytics algorithms find patterns and trends in how healthcare workers use the necessary supplies for patients’ treatment. In addition, data analytics software records that a provider has ordered resources from a supplier that fully meets the needs of patients. For example, an implant for a joint replacement surgery is the right size for a specific patient.
Identify Denied Claims
If an insurance company denies a provider’s claim, the provider must appeal for an additional fee or investigate the denial and correct the claim. If the provider files an appeal, it costs four times more to process than if the claim was filed correctly for the first time. Predictive analytics algorithms help healthcare organizations save thousands of dollars. They analyze patterns of denials and underpayments and identify frequent mistakes and suspicious issues. It doesn’t matter whether the provider made a mistake in choosing a service code or the insurer denies a disproportionate number of claims. The analytics system prepares a report for the provider based on which they can take action and prevent potential denials.
Justify Compensation Needs
A provider may accidentally make a mistake in coding a service or provide incomplete data to a payer. But the Centers for Medicare & Medicaid Services (CMS) may still suspect that the error was not an accident. After all, there have been cases where some Medicare Advantage providers deliberately assumed that a particular patient’s health problem was more severe than it was. They hoped to receive higher compensation from CMS. With data analytics software, a healthcare organization can provide the payer with evidence of its compensation needs. Algorithms analyze and transform the data into transparent reports of patient conditions throughout the calendar year. Via reports, the state or government entity calculates the appropriate compensation.
Solve and Prevent Compliance Problems
Healthcare organizations must comply with healthcare requirements and standards. When it comes to protecting patients’ data, the Health Insurance Portability and Accountability Act (HIPAA) plays an important role. Its effect is reinforced by the Health Information Technology for Economic and Clinical Health (HITECH) Act. Failure to comply with these laws can result in legal costs and fines for a healthcare organization. Data analytics tools with AI and ML solutions identify deviations from established standards and compliance requirements in real time. In addition, they simulate possible hacker attack scenarios. Thanks to this, the provider can develop a plan on what to do if a real hacker attack occurs.
In Addition
The number of data-driven healthcare organizations has increased. Forbes writes that investments in analytics and insights are a priority for most healthcare players. However, only sixteen percent of respondents consider their position mature. They skillfully use analytics solutions to obtain and process info from multiple sources and make quick decisions. This process is still quite difficult for other healthcare companies.
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.
