According to Statista, the predictive analytics market is expected to reach $41.5 billion by 2028. Such an interest is driven by the fact that this technology helps analyze large data volumes to gain insights businesses can apply for a competitive advantage.
When predictive analytics meets healthcare, this creates the foundation for medical companies to achieve peak performance through optimizing workflows, minimizing overhead, and improving overall productivity. In the end, all these aspects lead to better patient care.
To make sense of advanced analytics in healthcare, it is crucial to explore the approach’s tech foundation, determine the relevant software’s critical components, and learn how to develop one in successive steps. The goal is to clearly understand the process behind developing healthcare analytics solutions to get the phenomenon’s benefits.
Predictive Analytics
Regarding the technological foundation behind healthcare analytics software, one should explore several key insights. In short, predictive analytics is a method of using data to predict the occurrence of particular events. In healthcare, predictive analytics uses data streaming to process medical information to calculate and quantify potential patient outcomes.
Healthcare analytics solutions usually work with electronic health records (EHRs). The algorithms tap into the data sets having all the relevant patient information and deduce insights into how a patient’s condition might change. So, these tools can determine the specific signs of disease early. That makes the healthcare predictive analytics market booming (see Fig.1).
The phenomenon gains traction due to techs like Machine Learning (ML) and Cloud Computing. These help train the algorithms behind prescriptive analytics to be more accurate and access patient data more securely. As a result, predictive analytics works with data to translate it into actionable and understandable insights.
When coupled with healthcare, it means better patient outcomes and an ability to optimize the overall system organization. Data is the most valuable commodity, and business analytics in healthcare is a direct way to use it. To tap into the technology, you need a particular tool with some key features.
Key Components of Healthcare Analytics
At the moment, advanced analytics in healthcare works with data. Yet, when looking deeper, these can do much more than that. Let us look at some key components healthcare analytics software should have.
Data Collection and Integration
As it was mentioned, healthcare predictive analytics algorithms tap into EHRs to get access to patient information. Besides, a healthcare analytics platform must directly connect with EHRs to access digital versions of patient medical history elements, like diagnoses, prescriptions, test results, and treatments. The relevant algorithms can also work with wearable and Internet of Things (IoT) data from fitness trackers, glucose monitors, smartwatches, smart inhalers, and connected pillboxes. These collect real-time health data outside the clinical setting.
Data Preprocessing and Cleaning
After patient information is gathered and integrated, it is time for data preprocessing and cleaning. This component comes in three forms:
- Data quality assessment. To have accurate predictive models, one must ensure high-quality data is processed. The data must be checked for errors, inaccuracies, inconsistencies, incorrect values, and duplicate entries.
- Missing data handling. Missing data might result in incomplete or biased predictions. That is why data imputations are used to identify the missing information.
- Data standardization and normalization. This aspect involves translating data into a consistent format to make further analysis possible.
After the data is preprocessed and cleaned, it is time to move to predictive modeling.
Predictive Modeling
This component is based on two main approaches – ML and Deep Learning. In short, ML techniques used within predictive modeling correlate with supervised learning, unsupervised learning, and reinforcement learning. These label data to learn from the relationships between input and output. Besides, it helps identify patterns with unlabeled data and receive feedback from the external environment.
As to Deep Learning, this approach entails working with neural networks, convolutional neural networks (CNN), and recurrent neural networks (RNN). These computational models mimic human brain functions used to model complex patterns within data. Besides, these methods help work with textual data, images, and sequential data.
Data Visualization and Reporting
After predictive modeling has done its job, the technology should translate the findings into insights humans can understand. That is the moment where data visualization comes into play. Essentially, it is achieved through these instruments:
- Interactive reports and dashboards. They provide users with a visual representation of predictive analytics results. Often, healthcare analytics solutions offer features like real-time updates and controls, allowing users to filter data based on specific criteria.
- Temporal and geospatial visualization. Geospatial data is displayed on maps, while temporal data shows how the data changed over time, which can help identify patterns on a broader geographical and temporal scale.
- Customizable data presentation. The final aspect enables users to customize and change the output data based on specific preferences and needs. For example, one can select different variables, adjust data granularity, and choose the visualization approach.
The component above represents the core of any healthcare predictive analytics solution. As one can see, there is a straightforward process involved:
- The data is gathered from various sources.
- The data is checked for errors and cleaned.
- Predictive models establish patterns within data.
- Predictive analysis results are illustrated in a proper format.
Healthcare Analytics Software Applications
One of the key reasons for developing healthcare analytics software is to know what particular benefits it can bring and where you can apply it. Here are some popular use cases:
1. Disease Risk Prediction and Prevention
Predictive analytics helps detect early signs of disease. Taking this point even further, one can suggest that tech creates the foundation for timely treatment and intervention to increase the chance of successful recovery. For instance, when ML algorithms within predictive analytics are coupled with Big data, practitioners can analyze medical imaging data to detect early signs of tumors. Respectively, this increases the chance of preventing the disease from progressing.
2. Personalized Medicine
There are massive strides toward more personalized medicine. The advent of telemedicine and mHealth analytics stands behind the relevant market expansion (see Fig.2).
In the context of healthcare data analytics, it can contribute to the development of personalized medicine with these two approaches:
- Pharmacogenomics. This method shows how a patient’s genetic makeup influences a person’s response to a particular medication. By fueling genomic data into predictive analytics algorithms, doctors can identify the most effective medication for a patient. That minimizes the chance of adverse effects and maximizes the drug’s effectiveness.
- Tailored treatment plans. Predictive analytics help create tailored-to-particular-patients treatment plans. Tech accounts for factors like genetics, comorbidities, and lifestyle. That provides the precondition for building treatments working best on a specific patient.
As a result, healthcare data analytics stands at the forefront of personalized medicine. However, looking at technology from a more general perspective, it can also have a collective impact.
3. Public Health Management
In public health, healthcare analytics solutions can be used to identify high-risk populations and better resource allocation. For instance, tapping into various data collection tools, including ChatGPT, predictive analytics algorithms can access various social determinants of health to address health disparities and identify at-risk populations.
Advanced analytics in healthcare also ensures that the relevant services are directed toward the most needed areas. To illustrate, the tech can predict the demand for hospital beds or emergency services to help administrators adjust staffing levels and meet equipment needs accordingly. These create more streamlined operations and workflows.
With the healthcare analytics solutions applications behind us, it is time to speak about the steps for their development. These will show what you must do to bring a top-performing platform to life.
Step-by-Step Healthcare Data Analytics Software Development
Healthcare is an excellent place for experimenting with innovative solutions. But to ensure there are no tall orders, it is vital to follow specific steps in developing software, like the one with healthcare data analytics involved. Thus, there are five key steps to follow.
Step 1. Requirements Definition
Any project’s success directly depends on how clearly defined the project requirements are. These guidelines indicate what the product will do, how it will do, and what stakeholders are involved. To better understand the requirements, gather feedback from stakeholders. It will show what business analytics in healthcare should achieve. Besides, you can gather input from personnel who have experience working with predictive analytics. That grants a perspective on what functions to pursue. As a result, you will have project requirements outlined and ready.
Step 2. Research and Discovery
Project discovery and the research phase are two aspects of the next important step. In short, the discovery phase includes an array of in-depth consultations with various professionals, such as project managers, business analysts, and the development team. These are held to communicate every given aspect of the software’s functionality.
Within the research aspect, you have a well-detailed look into the market and competitors. Market analysts gather data that transform the scope of work, determine the tech stack used, and have an approximation of the budget needed. Both aspects above outline the overall scope of work and segment it into particular manageable and achievable steps.
Step 3. Designing and Prototyping
The next stage falls on the shoulders of designers. Designing encompasses preparing initial designs, mockups, and prototypes. The last elements are clickable and show how the future healthcare analytics platform performs when interacting with a user. Designing good prototypes is a great way to gather user feedback and adjust modules based on the input. That ensures the software will look and behave as planned.
Step 4. Development, Implementation, and Testing
With requirements, market insights, designs, and prototypes, it is time to develop a Minimum Viable Product (MVP). That is the first actual iteration of the product. MVPs help deliver the platform’s key functionality quickly. When MVP is developed, you can test it in real-life conditions. After handling the MVP, you gather feedback and adjust the product accordingly.
With the insights from the MVP, one can start implementing the end product within an existing system. You must do that to ensure the overall continuity of operations. In such a case, your staff can learn the new tool and adjust to its workings while flattening out the steepest learning curves. At this point, operational healthcare data analytics software becomes an indispensable part of the learning process, significantly facilitating it.
Step 5. Support and Maintenance
Yet, even when healthcare analytics software is developed and implemented, there are no preconditions to stop. In turn, it is time to start the support and maintenance process. In other words, you need to continue gathering insights on real-world product usage. These can be collected from approaches like user testing.
Furthermore, based on the insights, you can restructure workflows accordingly or tweak features to present a better user experience. In any case, with good healthcare data analytics software always comes continuous support and maintenance. Remember, choose development partners or vendors that can provide top-notch product design, management, and support.
Off-the-Shelf and Custom Healthcare Analytics Software Solutions
Organizations may want to avoid developing healthcare data analytics software from scratch. In turn, businesses might look for custom solutions valuing competitive advantage the most. Thus, one should speak about the difference between off-the-shelf and custom-developed tools to know which matches your case the best. Respectively, let us look at each side of the argument and determine their pros and cons. That helps make the final choice.
Off-the-Shelf Software
In a nutshell, choosing off-the-shelf healthcare analytics software means selecting among a broad range of pre-configured options. While these fit many cases, they are built based on something other than your project requirements. As a result, with off-the-shelf solutions, you might face the challenge of trading particular features for a shorter integration process.
Overall, off-the-shelf healthcare analytics software comes with these pros and cons:
Pros:
- Broad array of features to choose from
- Comprehensive support documentation
- Interoperability with numerous third-party systems
Cons:
- Low-degree of customization
- Steep learning curve
- Dropping the project will be costly
Therefore, off-the-shelf solutions are a good fit for organizations and businesses that find general healthcare analytics features sufficient and want the product to run as soon as possible.
Custom-Developed Software
Developing custom solutions takes longer, but it follows your project requirements and provides the product precisely as planned. Since many healthcare organizations have unique needs and workflows, they should select a tailored-to-particular-needs healthcare data analytics platform. In this case, one can also suggest custom solutions come with these pros and cons:
Pros:
- Built with your specific needs in mind
- It comes with extensive customization options
- More affordable due to projects being split in time
Cons:
- Take a great deal of time to be developed
- Need to be integrated into the system
- Lacks support documentation
All in all, when selecting a custom healthcare data analytics software, you get greater control over the entire development process. And it needs less investment. With the off-the-shelf tool, you get the product to the market quickly and only need to spend a little time developing support documentation. However, in both cases, regulatory and ethical considerations must be made.
Regulatory and Ethical Considerations in Predictive Analytics
Healthcare is a highly-regulated industry. That is because its professionals work with sensitive patient data. Thus, you must consider data privacy and ethics when developing a healthcare analytics solution. When deconstructing them, you need to take care of these aspects:
HIPAA
If your product deals with patient data, it must comply with HIPAA. That is relevant for telehealth and advanced analytics in healthcare alike. Otherwise, noncompliance can lead to hefty fines (see Fig.3).
HIPAA is a great way to follow standards for protecting the privacy and security of patient data. Within predictive analytics, HIPAA is implemented by restricting unauthorized access to EHRs and safeguarding identifiable patient health information.
GDPR
If you plan to have clients from Europe, it is vital to adhere to GDPR principles. The standard protects patient health data by forcing providers to obtain explicit consent for data processing and appointing a Data Protection Officer (DPO). In other words, when using predictive analytics, patients must be informed about how their data will be used. Respectively, a person must consent to the technology to access patient health information.
Data Anonymization and Encryption
When protecting patient privacy, one cannot avoid data anonymization and encryption. The first approach encompasses transforming identifiable patient data, while the latter helps encode data to prevent unauthorized access. In healthcare, information must be used with great caution. To illustrate, a healthcare provider might anonymize patient data within EHR by removing names, addresses, and other personal identifiers. As a result, a predictive analytics algorithm will not know whose data is being processed.
Fairness and Bias
One of the ongoing ethical challenges associated with prescriptive analytics is the bias of predictive models. In other words, technology can present data in a particular way to benefit specific groups of people. To deal with this issue, predictive models used in healthcare analytics software must be trained and tested on diverse populations to minimize bias.
Patient Consent
Finally, one should understand that most healthcare analytics solutions work with patient data. Thus, even if you do not adhere to GDPR, the factor of patient consent is still highly relevant. When using PHIs, people must be informed. Besides, healthcare vendors must ensure patients whose data is fed into predictive algorithms understand all the associated risks and benefits.
Healthcare Analytics Future Trends
At this point, the only thing left with advanced analytics healthcare is to know where this tech is moving. There are particular directions in which predictive analytics in healthcare can navigate.
Automated Machine Learning (AutoML)
Along with generally used ML aspects of predictive analytics, there is a growing popularity for AutoML. It applies ML models to deal with real-life scenarios through automation. It means the technology automates the selection and parameterization of ML models to deal with the rapidly growing influx of data. Within healthcare analytics solutions, autoML helps to automatically address the massive influx of data the world is witnessing.
Blockchain Analytics
Blockchain is a powerful technology with numerous potential applications. That may be why the blockchain market is one of the fastest growing (see Fig.4).
Blockchain can help inspect, cluster, identify, model, and visualize vast patient data. Also, the inherent processes within blockchain focus on transparency, security, and independence, the critical factors to ensure data privacy and protection.
Explainable AI (XAI)
Artificial Intelligence (AI) is almost everywhere now. Therefore, there is also a space to apply the tech to healthcare predictive analytics. One of the new AI interactions is Explainable Artificial Intelligence (XAI). This approach helps make healthcare data analytics work in a non-linear manner to avoid “black-box processes.”
Augmented Analytics
Finally, augmented analytics is a future trend based on AI and ML that improves the degree of human intuition across the entire analytics lifecycle. The approach makes analytics accessible to more people to raise the value of data. With augmented analytics, users can interact with predictive analytics algorithms and have a two-way conversation, especially if Natural Language Processing (NLP) is involved.
The Bottom Line
Advanced analytics in healthcare has much to offer as it helps make sense of a rapidly growing amount of data. With the right approach and knowledge, you can benefit from predictive analytics solutions and improve patient outcomes and organizational workflows. Thus, medical organizations and businesses that adopt healthcare data analytics most effectively will have the most competitive advantage. If you want to be among those, contact us, and our experts will ensure healthcare data analytics works for your sake.
