How Predictive Analytics Changes Healthcare Landscape
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. Let us look at the pros and cons of predictive analytics in healthcare and how it has already changed the industry worldwide.
What is Predictive Analytics?
Predictive analytics is a branch of AI-powered analytics that utilizes the available data to predict possible future events and activities that might influence these events. Data scientists and analysts use various techniques such as data mining, modeling, statistics, AI-powered predictive models, and machine learning to evaluate historical and real-time data inputs. Based on data analytics, the system can offer predictions on the potential outcomes of specific actions or future events related to the previous data entries.
Predictive modeling consists of four major steps:
- Data collecting and cleansing – Data experts collect all the available information from the clinical records and other data sources. Data is then cleansed to remove redundant information that brings no value. The bulk of valuable data is then formed and stored in the data storage.
- Data analysis – To build a predictive model, the experts need to understand how parts of data relate to one another and how they influence each other. Which actions trigger certain outcomes? Building a data chart that contains the information and reflects the relations in it allows the analysts to see trends.
- Building a predictive model – Data may or may not land on specific analytical algorithms or models. The experts will use relevant data algorithms that match your needs and ensure all data sets match the selected algorithms.
- Incorporating the model into your process – The data model is then integrated into a specific business or organizational context. That way, the clinical staff receives a full functional predictive model that can run smoothly through the available data sets.
With the predictive analytics model set, clinics can significantly boost performance efficiency. But what exactly are the benefits of predictive analytics, and what are its drawbacks?
What is the Role of Clinical Information Analysis in the Modern Healthcare Industry?
In industries where the outcomes of each decision might have an increasingly high impact on human well-being and even survival, it is especially critical to make clear and precise predictions. Healthcare is one such industry that relies heavily on the quality of analytics and forecasts. That is why using advanced AI-powered tools to improve treatment outcomes is crucial. The potential data analytics use cases in healthcare can bring many benefits within the clinical environments and beyond:
- Health care digitization – having fully digitized health records allows for the application of big data storage in the cloud. The data stored in the cloud environment enables quick access and processing critical for advanced data analytics.
- Ease of workflows – with faster access to digitized and sorted data, clinical staff can optimize most of the data-processing workflows, boost access to information, and lower operations costs.
- Medical risk intelligence – with hospital data analysis, it becomes easier to cut financial and organizational risks. Most importantly, however, it enables physicians to cut patient health risks.
- 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 pros and cons of predictive analytics in healthcare, we must also admit that there are certain risks of the widespread application of this practice.
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 Information Analytics Benefits the Doctors and the Patients
In broad strokes, we have discussed some of the pros and cons of predictive analytics in healthcare. Let us see its practical benefits for doctors and patients. Advanced technology allows for the reduction of health risks, improved cost-efficiency of clinics, streamlined communication, and better forecasting.
The US Hospital Readmission Reduction program subjects healthcare organizations to penalties in case of patient readmission in 30 days following initial care. Predictive analytics help hospitals identify the patients with a high risk of readmission after each particular type of treatment. Analytical tools allow the doctors to determine whether to put the patient in a high-risk category.
With predictive analytics, doctors can change and adjust the treatment or prescribe the time of a follow-up visit to avoid readmission. More so, 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.
Prevent Self-Harm and Suicide
Identifying patients who might be prone to self-harm or exhibit subtle suicidal tendencies is vital. That allows doctors to offer mental health care to such 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 electronic health records (EHR) 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 potentially save thousands of lives.
Precision Medicine and Patient Reaction Estimates
The researchers now turn to predictive analytics and modeling as a way to supplement the traditional research processes. One exciting way they do that is by entering extensive amounts of patient data, including their reactions to treatment, with the specification of many individual factors. The analytical tools then give predictions on the responses of hypothetical patients to specific procedures and medications.
The researchers can study the effects of different medications and procedures without involving actual patients. The combination of hospital data analysis and machine learning allows scientists to run simulations on the progression and treatment of such degenerative conditions as Parkinson’s and Alzheimer’s.
Improve Decision-Making in Clinics
Physicians can apply predictive models to help make better decisions regarding patient care. As one of the most common data analytics use cases in healthcare, it can help identify patients at risk of developing harmful conditions based on their current situation and recommend plans for preventing them.
Predictive modeling is also critical for preventive healthcare in chronic conditions as they are the main reason for excessive spending on healthcare. That is especially important in light of such dangerous chronic diseases as obesity plagues developed societies. With modern tech, doctors can build solid predictions on the development of chronic conditions in individual patients based on their habits, occupational hazards, level of physical activity, and other factors. With detailed patient profiles and analytical tools, doctors can develop individual plans for the patients to prevent harmful and even lethal outcomes of chronic diseases.
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. Medical risk intelligence can help identify the clinic’s inventory needs. Clinic administrators can, thus, buy the necessary goods upfront or locate them in the right places to avoid stockouts. It can also help with overstocking if the clinic does not use much of certain goods.
Improved inventory management boosted by hospital data analysis tools can prevent redundant spending and the loss of perishable materials that are not being used. Analytics can also help predict the seasonal needs of the clinic. That way, clinics can save more by reducing waste and stocking efficiently.
Improve Patient Engagement
Applying machine learning for patient treatment can also help identify the patients with a higher risk of no-shows. Patient engagement and adherence to the treatment plan are vital for successful treatment. Predicting no-shows helps doctors emphasize the importance of engagement for certain patients. The clinic can send appointment notifications to the clients and reminders before the planned visit.
Additionally, the clinics can send notifications to the patients who currently undergo medication treatment away from the clinic. For instance, the patients may receive messages that remind them to take their prescribed pills.
Personalize Treatment Practices
Traditionally, the healthcare industry 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 can help with them. But! The doctors can now prescribe a more personalized treatment as each patient has their history stored in a database analyzed by special tools. Each procedure and each medication can be tailored and dosed to the needs of each particular patient.
Manage the Population Health
On the opposite side of the benefits of hospital data analysis, the researchers can access larger bulks of data and analyze the healthcare situation on a broader scale. Analytics tools can compile data from thousands upon thousands of clients and predict population health in response to global events. Recent events have proven that there will be more data analytics use cases in healthcare that could help deal with international healthcare threats.
Reduce the Overall Cost of Healthcare
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 global scale, predictive analytics can help reduce healthcare costs.
A single clinic can save up considerable amounts of money by first improving inventory management and then boosting the efficiency of patient care. A healthy patient who receives proper care and does not return to the clinic is a happy patient who does not cost money to the healthcare system.
On a larger scale, 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 participate in the country’s economy, are more productive, and are generally more 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 is a powerful tool, but it is not a silver bullet. There are limitations and challenges to the adoption of this technology that may lead to undesirable consequences, and any clinic administrator has to keep those in mind:
Healthcare providers need to ensure seamless integration between many systems, including EHR, enterprise resource planning (ERP), customer relations management (CRM), and more. Smooth integration of all the systems enables uninterrupted data aggregation, which plays a critical role in the overall success of the predictive models.
Structured and unstructured data sets need to be appropriately added to the overall data bulk, making the process even more complicated. Unstructured data might require using such advanced technology as natural language processing (NLP) and computer vision. And to make data accessible to all the stakeholders, you need to correctly structure and visualize it, which opens the door for further technology challenges.
Cybersecurity and data privacy, especially when it comes to data that is being shared among multiple stakeholders across organizations, is one of the major concerns for healthcare practitioners and patients alike. For that reason, any software solution intended for implementation in the healthcare industry must comply with HIPAA regulations.
Developing HIPAA-compliant software is quite challenging, so it calls for unparalleled expertise in building predictive analytics solutions. SPsoft, for instance, develops fully HIPAA-compliant healthcare software that guarantees complete data security.
Predictive models can be relatively efficient, but they are not worth a dime without people who can leverage their capabilities. You cannot put healthcare practitioners out of the equation as they produce data inputs and ultimately make decisions based on the predictions.
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 adoption of predictive modeling comes with a fair bit of challenges. In addition to the issues discussed above, the human factor is the main flaw. A predictive analytics tool can give the correct answers. Still, it cannot ensure treatment success without the doctor’s decision and the patient adhering to the treatment plan.
Human factors can manifest themselves in various ways, from patients forgetting to take pills at a prescribed time of the day to patients simply not wanting to receive the care they need. Doctors can make mistakes too, so there is no guarantee the system will work in each case.
The value-based outlook on the healthcare industry pushes more and more organizations to apply predictive analytics to develop improved and personalized treatment plans, research new treatment techniques, reduce readmissions, cut the cost of operations, and improve the overall outcomes of the treatment.
Of course, the future of AI, machine learning, and other advanced technologies would offer an even broader scope of possibilities. Clinics will improve facility administration, optimize workflows, establish transparent supply chains, and make more 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.