Predictive analytics plays a vital role in the global healthcare industry’s evolution, improving operational management, patient engagement, quality of clinical research, diagnostics, and more. Inherently intertwined with the technological advancement of our civilization, the healthcare industry takes advantage of the latest innovations. Predictive modeling is one such innovation that reshapes the landscape of modern healthcare, saving lives and maximizing the efficiency of clinical environments.
As predictive analytics is growing in its applications across industry domains, including fintech, retail, and more, healthcare practitioners find its uses in the clinical environment. Predictive analytics has become a cornerstone of the digital health revolution. So, let’s look at the pros and cons of predictive analytics in healthcare and how it is changing the industry worldwide.
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Table of Contents
What is Predictive Analytics?
Predictive analytics is the use of available data to make predictions about possible future events and activities that might influence these events. It is a sophisticated form of data analytics that involves a branch of AI-powered analytics. Data scientists and analysts utilize various predictive analytics techniques, such as data mining, modeling, and statistics to evaluate historical data and real-time inputs. By using AI-powered predictive models, the system can offer a forecast on the potential outcomes of specific actions. Predictive analytics uses historical records to identify patterns, allowing researchers to create predictive models that simulate future outcomes.
The Predictive Modeling Process in Healthcare
Predictive modeling consists of four major steps, often employing a statistical technique like regression to ensure accuracy:
- Data Collecting and Cleansing. Experts collect information from clinical records and other predictive data sources. To ensure a high-quality predictive model, redundant information is cleansed.
- Data Analysis. Experts evaluate how variables relate to one another. They might use historical data to identify trends. This is where types of data analytics like descriptive analytics (analyzing what happened) meet advanced analytics.
- Building a Predictive Model. Experts select relevant modeling techniques. Common predictive analytics techniques include regression models and classification models. In some cases, deep learning and complex learning algorithms are applied to process unstructured data.
- Incorporating the Model. The predictive model is integrated into the clinical workflow. This is where predictive analytics work begins to show value, providing real-time predictive insights to the staff.
With the predictive analytics model set, clinics can significantly boost performance efficiency.
What is the Role of Clinical Information Analysis?
In industries where decisions impact human survival, the use of predictive analytics is crucial. Predictive analytics provides the clarity necessary to improve treatment outcomes. While descriptive analytics explains the past, predictive analytics provides a roadmap for the future, and prescriptive analytics suggests the best course of action.
- Health Care Digitization. Digitized records allow for big data storage, enabling predictive data analytics to run on a massive scale.
- Streamlined Workflows. With faster access to sorted data, clinical staff can optimize workflows and lower costs. Predictive analytics enables hospitals to forecast patient flow effectively.
- Medical Risk Intelligence. By using a statistical model, it becomes easier to cut financial and organizational risks. Predictive analytics helps physicians cut patient health risks through early prediction of complications.
- Healthcare waste management – more efficient use of clinical resources and faster development of new drugs are also possible thanks to analytics.
- Reducing preventable deaths – improved quality of care results in the most crucial benefit of analytics – reduced number of avoidable deaths in clinics. That is the ultimate goal of medicine as a whole, and it can be achieved thanks to advanced tech.
Discussing the benefits of predictive analytics, we must admit there are risks. Predictive analytics is often criticized for its complexity.
- Centralization of Data. Storing vast amounts of predictive data on the cloud raises security threats. Predictive analytics software must be secondary to robust security.
- Ethical Concerns. The use of machine learning for patient treatment raises questions about accountability. Can a statistical technique truly replace human judgment? These concerns are why predictive analytics is an advanced tool that must support, not replace, the doctor.
Risks of Applying Predictive Analytics in Healthcare
- Centralization of data – given that the ever-growing amounts of data are being stored on the cloud that can be accessed via the internet, security threats have become increasingly alarming.
- Ethical concerns – the application of AI and machine learning for patient treatment raises eyebrows among the patients and the general public. Who makes the decision? Is it the machine, or is it the doctor? Who will be responsible if anything goes wrong? Can a machine make a better decision than a human? All of those questions and more raise significant ethical concerns for obvious reasons.
The 9 Ways Clinical Data Analytics Benefits Doctors and Patients
Predictive analytics allows for the reduction of health risks and streamlined communication. Let’s explore specific predictive analytics examples in the modern clinic.
Readmission Avoidance
The US Hospital Readmission Reduction program penalizes organizations for readmissions within 30 days. Predictive models help hospitals identify high-risk patients after each particular type of treatment. Predictive analytics enables doctors to adjust the treatment plan before discharge, utilizing regression analysis to see which factors lead to readmission. Also, analytical tools can account for the external factors that might influence a patient’s condition – social factors, medical history, working conditions, habits, and more. Avoiding readmission not only improves the patient’s satisfaction and care outcomes but also saves clinics money.
Self-Harm and Suicide Prevention
Predictive analytics helps identify patients exhibiting suicidal tendencies. Teams combine this tool with EHR data to red-flag high-risk individuals. That allows doctors to offer mental health care to patients, which can hopefully prevent them from committing suicide or self-harm.
Suicide is a real problem, as its rates have skyrocketed in the US by more than 30% between 2000 and 2020. Mental Health Research Network conducted a study using predictive modeling combined with EHRs and standard questionnaires to identify patients who fall into the high-risk category regarding suicide and self-harm. They discovered that suicide or self-harm attempts were 200 times more likely among the patients red-flagged by the algorithm. That is one of the most impressive data analytics use cases in healthcare, as it can help save thousands of lives.
Precision Medicine and Patient Reaction Estimates
Researchers use predictive models to supplement traditional research. They use learning techniques and extensive amounts of historical data, including the reactions to treatment. That allows for making predictions on how hypothetical patients respond to medications and different procedures. Such a type of predictive research enables scientists to perform simulations of degenerative conditions like Parkinson’s and Alzheimer’s without initial human trials.
Improvement of Clinical Decision-Making
Predictive modeling techniques help identify patients at risk of chronic conditions based on their current medical situation and recommend relevant plans for preventing them in the future. Predictive analytics allows doctors to develop preventive plans. Whether it is predictive data analysis on obesity or heart disease, AI helps develop personalized prevention strategies. With modern tech, doctors can build solid predictions on the development of chronic conditions in patients based on their habits, occupational hazards, level of physical activity, and other factors.
Operational Management
The size and complexity of a clinic and its services can impact inventory management efficiency and create bottlenecks that could potentially hinder the quality of care. Predictive analytics can help identify a clinic’s inventory needs.
By using predictive modeling techniques, administrators can avoid stockouts. Improved inventory management boosted by hospital data analysis tools prevent redundant spending and the loss of perishable materials that are not being used. At the same time, predictive models to forecast seasonal needs, like flu vaccines, help clinics save money and reduce waste.
Higher Patient Engagement
Companies use predictive modeling to identify the risk of “no-shows.” Patient engagement and adherence to treatment plans are vital for successful treatment, so predicting no-shows helps doctors emphasize the importance of engagement for certain patients.
Predictive analytics allows the clinic to send targeted reminders or notifications to the patients who currently undergo treatment away from the clinic. Thus, predictive analytics help determine which patients need extra engagement to adhere to their treatment plan, take their prescribed medications, and much more.
Personalized Treatment Practices
Traditionally, healthcare used a one-size-fits-all approach, which was far from ideal. Treatment procedures and drugs have been prescribed based on limited information about the patient and primarily relied on the general knowledge of symptoms and the processes that help with them.
BUT! Predictive analytics examines all actions of a patient’s history. Unlike the old one-size-fits-all approach, using predictive analytics to make clinical decisions ensures that each drug is tailored to the individual’s history. Predictive data analytics guarantees that the prediction of drug reactions is accurate.
Well-Managed Population Health
On a broader scale, predictive analytics provides a view of international healthcare threats. Predictive analytics can be applied to analyze the situation across thousands of clients, helping scientists forecast and respond to global pandemics. Recent events have proven that there will be more data analytics use cases in healthcare that can help address such global threats.
Reduced Healthcare Costs
With the ultimate goal of making healthcare services as efficient as possible, doctors can achieve better treatment outcomes for patients and save more money. From a single clinic and up to the larger scale, predictive analytics can help reduce healthcare costs.
Predictive analytics helps do that appropriately by improving efficiency in terms of providing care services and inventory management. A healthy patient who receives proper care and does not return to the clinic is a happy patient who costs no money to the healthcare system. In addition, adopting predictive models to identify waste and predictive analytics to reduce redundant spending ensure that funds are used where they matter most.
Ultimately, a healthy population that takes care of its health, eats healthy, does not succumb to harmful habits, and does not suffer from chronic conditions is a more efficient population. These people do not spend money on drugs but on other products, actively participating in the local economy. They are more productive and beneficial to the public. Such a population does not put much pressure on the healthcare system, saving money for the entire country and humanity.
Challenges to the Adoption of Healthcare Predictive Analytics
Predictive analytics work is complex: despite being a powerful tool, it is not a silver bullet. Companies use predictive analytics to stay competitive, but they face limitations and other hurdles on their way to the technology’s adoption. That may lead to undesirable consequences, and any clinic administrator has to keep those challenges in mind.
Technology
Healthcare providers need to ensure seamless integration of predictive analytics software with other critical systems, such as EHR, enterprise resource planning (ERP), customer relations management (CRM), etc. Their smooth integration enables uninterrupted data aggregation, which plays a critical role in the overall success of the predictive models.
Large data sets need to be appropriately added to the overall data bulk, making the process even more complicated. This requires advanced analytics capabilities and possibly natural language processing (NLP) for unstructured data. Besides, making data accessible to all the stakeholders requires its correct structuring and visualization, which opens the door for further technology challenges.
Security
Cybersecurity and data privacy, especially when it comes to healthcare data that is often shared among multiple stakeholders, is one of the major concerns for practitioners and patients. For that reason, any predictive analytics software must be HIPAA-compliant.
Developing HIPAA-compliant healthcare software is quite challenging, so it calls for unparalleled expertise in building predictive analytics solutions. After all, predictive analytics provides value only if the sensitive data is protected.
People
Predictive models are efficient, but they are not worth a dime without people leveraging their capabilities. You cannot put healthcare practitioners out of the equation as they produce data inputs and ultimately make decisions based on the predictions. Thus, predictive analytics work depends on the human element. Although AI can make predictions, doctors must act on them.
For the long-term success of predictive analytics within a clinical environment, all of these issues must be addressed by both technology enablers and healthcare providers.
Predictive Analytics is Challenging but Worth the Effort
The value-based outlook on the healthcare industry pushes more and more organizations to apply predictive analytics for:
- Developing improved and personalized treatment plans
- Researching new treatment techniques
- Reducing readmissions
- Cutting the cost of operations
- Improving the overall treatment outcomes
Of course, the growth of AI, machine learning, and other advanced techs means that predictive analytics can provide even much broader possibilities. Clinics will improve facility administration, optimize workflows, establish transparent supply chains, and make cost-efficient decisions.
Despite the challenges, the value presented by predictive analytics is immeasurable. The technology is worth the investment if you partner with a reliable tech provider. Overcoming human factors will require time and dedication. Still, the technical side of the problem can be resolved if your partner has a team with solid expertise in healthcare predictive analytics.
The future of healthcare is proactive, not reactive. Do you want to use predictive analytics to reduce readmissions or build advanced AI-driven diagnostic tools? SPsoft has the expertise to make it happen!
FAQ
What are the main types of predictive analytics models used in healthcare?
There are primarily two types of predictive models: classification models and regression models. Classification models are used in predictive analytics to place data into categories, such as “high risk” or “low risk” for a specific disease. Regression models, on the other hand, make predictions about continuous values, such as the predicted length of a patient’s hospital stay. Predictive modeling techniques also include time series models to forecast future trends based on chronologically ordered historical data.
How does predictive analytics work with machine learning?
Predictive analytics work by combining statistical methods with machine learning. Machine learning involves learning algorithms that improve their prediction accuracy over time as they are exposed to more data. For example, a regression model might use machine learning to identify complex correlations in a patient’s EHR that a human might miss. This technique used in advanced analytics allows the system to make predictions that are far more accurate than traditional statistical modeling alone.
Why is predictive analytics important for hospital waste management?
What makes predictive analytics important? It allows administrators to forecast resource needs with precision. By using predictive modeling techniques, a hospital can use predictive analytics to determine exactly how much of a specific medication or surgical supply will be needed during a flu season. This predictive analysis prevents overstocking of perishable items and stockouts of life-saving equipment. In this context, predictive analytics helps streamline the supply chain and significantly reduces the financial burden on the healthcare system.
Can predictive analytics be used for marketing strategies in healthcare?
Yes, predictive analytics can be used to develop marketing strategies and improve marketing and sales for private clinics. For instance, businesses use predictive analytics to analyze patient demographics and behavior to better understand which virtual care services are in high demand. Predictive analytics determines which patients are most likely to benefit from a new wellness program, allowing the clinic to use predictive analytics to make more efficient outreach decisions and improve overall patient engagement.
What are common predictive analytics techniques for patient data?
Common predictive analytics techniques include data mining, regression, and the use of classification models. Data mining techniques allow analysts to sift through massive amounts of historical data to find hidden patterns. Another analytics technique is the use of time series models, which is particularly used in predictive analytics to track the progression of chronic diseases. Predictive modeling techniques are also increasingly incorporating deep learning to analyze medical imagery, such as X-rays and MRIs, for early prediction of tumors.
How does predictive analytics provide insights into population health?
Predictive analytics provides insights into population health by aggregating historical data from diverse sources. Predictive analytics allows researchers to make predictions about the spread of infectious diseases or the rise of lifestyle-related conditions across a specific region. This form of data analytics is vital for public health officials to forecast the impact of global events and develop predictive analytics to improve the allocation of public health resources. With advanced analytics, they can use predictive analytics to find the root causes of health disparities.
What is the role of historical data in predictive analytics?
Historical data is the foundation upon which every predictive model is built. Without a robust set of historical data, the system cannot use historical data to learn which variables lead to specific future outcomes. Predictive analytics uses historical records (past patient reactions, hospital stay durations, and treatment success rates) to train machine learning models. Only by looking at what has happened can predictive analytics make accurate assessments of what is likely to happen next, making the prediction reliable.
How does predictive analytics help in reducing healthcare costs?
Predictive analytics helps reduce healthcare costs by identifying inefficiencies and preventing expensive complications. For example, predictive models to identify patients at risk of readmission allow doctors to intervene early, preventing the penalty associated with the Readmission Reduction Program. Additionally, using predictive analytics to determine the most effective treatment plan for a patient prevents the “trial and error” approach to medicine, which is a major type of predictive analytics cost-saver. Predictive analytics can help hospitals optimize every aspect of their operations, from staffing to inventory.
