The good old saying “prevention is better than cure” fits perfectly when we talk about predictive analytics in healthcare. The kind and level of transformation this technology has brought and continues to bring is truly remarkable.
With the growing adoption and implementation of predictive analytics in healthcare, medical experts now follow a proactive approach to diagnosing various diseases, making room for prevention and more treatment options.
The technology can collect and analyze unstructured patient data to bring critical insights that can assist with diagnosing, research, and treatments. However, this is not the only way predictive analytics shapes the future of healthcare.
Let’s dig deeper to explore other uses cases of applications of predictive analytics in the healthcare industry:
What is Predictive Analytics?
Predictive analytics is a branch of advanced analytics that leverages historical data, statistical algorithms, and machine learning algorithms to predict future outcomes by analyzing historical data. Here are the major components of predictive analytics you should know:
- Data Collection: The first component is about capturing relevant data from various sources.
- Data Cleaning: This involves ensuring the data’s accuracy and making it ready for analysis by removing or correcting any errors.
- Data Analysis: This component includes statistical methods to find patterns and trends in the data.
- Model Building: Model building incorporates creating models using machine learning algorithms that can predict future outcomes.
- Model Validation: This is about testing the model to ensure its accuracy and reliability.
- Deployment: It is about implementing the predictive model into business processes to facilitate informed decision-making.
- Monitoring: Monitoring involves continuously tracking the model’s performance and making changes as necessary.
Predictive Analytics in Healthcare: Top 15 Use Cases
Wondering how to use predictive analytics in healthcare or what are the use cases of predictive analytics in healthcare? Read the below points:
- Prevention and Early Diagnosis
Predictive analytics analyzes patients’ data to find risk factors and early symptoms of potential health issues. It allows healthcare professionals to take preventive measures that can avoid the occurrence of health issues in the future, saving lives and minimizing treatment costs.
- Chronic Disease Prediction
Predictive analytics along with machine learning algorithms makes a healthcare system capable of processing vast amounts of patients’ data to identify risk factors associated that can turn into chronic diseases such as cancer in the future. Based on the predictions, medical experts can monitor that particular patient’s health more closely to reduce the impact or likelihood of the health issue.
- Personalized Treatment Plans
The technology can be used to offer personalized treatment plans based on patient-specific data such as medical history. It improves the effectiveness of treatments, brings expected outcomes, and improves satisfaction. Predictive models can even suggest doctors with suitable medications and therapies for each patient.
- Patient Segmentation
Hospitals and other medical facilities deal with vast amounts of patient data that is difficult to segregate based on various parameters such as medical history, behavior patterns, and demographics. This technology can process enormous data to categorize patients tailor treatments and build communication strategies for each group of patients.
- Medication Adherence Prediction
Predictive analytics can be used to ensure medication adherence in patients by analyzing their behavior and medical histories. Based on this prediction, healthcare professionals take measures like personalized reminders or sharing of educational resources that can improve medication adherence for better outcomes, treatment effectiveness, and hospital admissions reductions.
- Predicting Length of Patients’ Stay and Reducing Readmissions
This advanced technology can analyze medical history, patient demographics, and treatment plans to predict the length of a patient’s stay in the hospital. With these details in hand, hospital staff can do efficient discharge planning while ensuring each patient gets proper post-discharge care and regular follow-ups, reducing readmissions.
- Research for New Treatments
Analyzing data from clinical trials and patient records, predictive analytics, along with machine learning models, can help with researching new treatments and predicting the potential outcomes of those treatments. Medical researchers can even ensure the efficiency and accuracy of these treatments.
- Controlling Contagious Diseases
With predictive analytics, it is easy to forecast outbreaks and identify high-risk areas to prevent the spread of contagious or infectious diseases. The technology can bring these results by analyzing data from social media, considering environmental factors, and more. Based on the given data, healthcare institutes can run vaccination campaigns and government can impose restrictions or run public awareness programs.
- Enhancing Patient Engagement
As aforementioned, this technology enables healthcare experts to personalize interactions and recommendations considering each patient’s data and understanding their specific health issues. These recommendations may include health tips, reminders to exercise regularly, or simply, content to educate them on the health issue or its solution. This type of communication makes patients feel involved and cared for and improves patient engagement.
- Drug Discovery and Development
One of the biggest applications of predictive analytics is drug discovery. By going through large unstructured and semi-structured datasets of clinical trials, patients’ health conditions, and more, the technology provides critical insights. Medical professionals can rely on these insights to accelerate the drug discovery and development process while improving the chances of successful outcomes.
- Preventing Fraud Healthcare Insurance Claims
Predictive analytics, when used together with machine learning, can make healthcare applications capable of auto-identifying unusual patterns and suspicious activities in health insurance claims. These apps can flag potential fraud cases and the organizations to take corrective actions and prevent financial losses.
- Resource Allocation and Management
One of the biggest use cases of predictive analytics is assisting healthcare providers with efficient resource allocation and management by anticipating patients’ demands, treatment requirements, equipment required, and available resources. It minimizes the overall healthcare service cost while enhancing patient care and experience.
- Supply Chain Management
Predictive analytics can help hospitals, medical institutions, and clinics with supply chain management. Apps and systems powered by this technology can forecast the demand for medical supplies and medications to enable healthcare service providers to maintain optimal inventory levels and minimize the risk of stockouts and overstocking.
- Predicting Patient Preference
It is critical to understand patients’ preferences and requirements to deliver better healthcare services and unmatched experiences. Predictive analytics can analyze demographic data, health behaviors, and shared feedback to enable hospitals and healthcare experts to render tailored services. Based on this data, they can even build effective communication strategies for individual patients. For example, one patient might look for telehealth services, whereas the other may prefer visiting the doctor physically.
- Predictive Tests for Ensuring Treatments’ Effectiveness
Hospitals, clinics, and medical professionals can build predictive analytics-powered systems or mobile applications that can predict blood and other tests to figure out the cause of health issues. Such systems can even analyze genetic information, biomarkers, and previous treatments to forecast what type of treatment would be suitable for a particular patient.
You might also be interested in: How AI Is Transforming the Healthcare Industry: Discover the Top Ways.
Benefits of Predictive Analytics in Healthcare
Now that you know the areas where implementing this technology can do wonders, let’s explore some amazing advantages of predictive analytics in healthcare:
Accurate Health Outcomes
Leveraging historical health data and machine learning models enables predictive analytics to offer accurate health outcomes. By historical data, we mean patterns in patient records, genetics, lifestyle factors, and more. Manually analyzing them is not only challenging and time-consuming but it may also question the accuracy of output. Predictive analytics can automate the process and the output provided can be used for offering early intervention and personalized treatment plans.
Enhanced Patient Experiences
Offering personalizing healthcare services significantly improves patient experiences. It also leads to more effective treatments. For example, predictive analytics can help medical facilities or hospitals identify patients who may require future follow-ups or re-admissions. Predictive analytics can streamline appointment scheduling, minimize wait times, and improve patient engagement.
Reduced Cost of Medical Care
Integrating predictive analytics in healthcare systems and applications can help with optimizing resource allocation and preventing expensive medical procedures. Doctors can implement cost-effective preventive measures by forecasting patients’ requirements and future complications or health issues.
Improved Staff Experiences
Using this technology, healthcare institutions can optimize workflow and reduce burnout to improve staff experiences. They can predict patient volumes and resources required to handle those patients to manage staff schedules and workloads efficiently. A supportive and well-organized work environment leads to job satisfaction and minimized turnover rates.
Streamlined Administrative Processes
With predictive analytics, medical facilities can streamline admin or internal processes. The technology analyzes operational data for better appointment scheduling, predicting no-show rates, and managing patient flow. Automating these operations not only minimizes the burden on the workforce but also leads to better handling of patients.
Common Challenges Associated with Implementing Predictive Analytics in Healthcare
Below are some challenges one can encounter while implementing predictive analytics in existing healthcare mobile apps or software or building a new one from scratch. We have also listed the solutions along with their respective challenges.
- Poor Data Quality
Predictive analytics solely relies on data to derive desired outputs. If the quality of the data collected from sources like lab results, wearable devices, and EHRs is low, it may impact the outcome of a prediction.
Solution: This problem can be addressed by implementing robust and reliable data governance practices, such as adopting a standard data format, implementing effective data cleaning and preprocessing techniques, and more.
- Lack of Data Privacy and Security
Since predictive analytics process enormous amounts of patient data that are highly sensitive, a lack of data privacy and security may lead to data breaches and other potential threats.
Solution: To overcome this problem, businesses can partner with healthcare app development companies that adhere to strict regulations like HIPPA to ensure end-to-end data privacy and security. Anonymizing and de-identifying data and conducting regular security audits are two other effective ways to address this problem.
- High Cost
It is one of the most common challenges many healthcare businesses face while implementing predictive analytics into their operations or systems. Developing and implementing this technology requires high technical expertise and considerable experience. Hiring experienced resources for this may be expensive. However, this one-time cost is definitely worth going for considering the immense benefits of this technology.
Solution: Partner with a reputed healthcare app development services provider that offers the option of MVP development to validate the app idea. Also, make sure the provider remains transparent about the pricing and has mentioned the upfront cost in their contract.
- Lack of Interoperability
Healthcare organizations may use multiple systems, built using different technologies, to perform diverse operations, such as drug discovery, health insurance management, patients’ management, and more. Lack of interoperability can be a challenge when implementing predictive analytics to such systems or applications.
Solution: Adopting interoperable standards and protocols that facilitate seamless data exchange and integration of predictive analytics into your existing infrastructure can be a solution to this problem.
- Resistance to Change
While most healthcare organizations are investing heavily in predictive analytics, many are still skeptical of implementing the technology due to various reasons including changes to established processes and job security.
Solution: Educating healthcare professionals about this technology by conducting training sessions and workshops can help them understand the technology and its benefits better.
How to Implement Predictive Analytics in Healthcare?
You need to connect with a reliable healthcare app development company to build a predictive analytics-powered app or solution from scratch or implement this technology into your existing healthcare systems.
The company will follow a stepwise approach to fulfill your project requirements and ensure you can utilize the maximum potential of this technology. But before you find a partner, keep the following tips in mind:
- Define your exact requirements like what areas you need to improve with predictive analytics.
- Search for reliable and experienced healthcare app development companies with hands-on experience in predictive analytics.
- Check their portfolio, customer ratings, and reviews to get an idea of their expertise and prior experience in predictive analytics.
- Now, shortlist a few of them and set up a demo call.
- Discuss everything in detail and get their perspective on how they are going to work on your project.
- Sign a contract with the one you find suitable for your needs.
- Make sure they offer post-launch support and maintenance services.
Tip: Conducting research before finalizing your healthcare app development partner can help you connect with the best one.
Conclusion
Predictive analytics is transforming the healthcare industry by facilitating early detection and prevention of health problems and improving the accuracy of treatments. However, these are not the only two applications of predictive analytics in healthcare. Read this blog to dig deeper into unique use cases of predictive analytics in healthcare.
Here we have also covered the benefits of implementing this technology into healthcare processes and finding the right healthcare app development company that can help you make the most of it.