Predictive modeling can deliver an extensive range of operational and security benefits. While also improving the experience and outcomes for your healthcare clients.
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Predictive modeling is a type of analytics that is primarily used to predict future events. Basically, this type of modeling relies on data analysis and artificial intelligence to identify and translate observed patterns. Within healthcare, this type of technology has revolutionized the ability to assess high-risk patients and determine any susceptibilities to diseases or illnesses. This is additionally assisted through the use of medical records and general demographic information, including age, medical history, and socioeconomic status. The ability to pre-emptively assess a patient’s risk of disease is helping pave the future for a new type of healthcare. After all, prevention is much more valuable than intervention! Given the possibilities offered by predictive modeling, it is essential to consider including it within your healthcare business startup guide. Patients are looking for healthcare solutions that have been modernized and embracing new technological advancements demonstrates your willingness to adapt.
When it comes to implementing predictive modeling within healthcare, the possibilities for improvement are endless. Some of the most common benefits we are currently seeing include:
Optimizing operational efficiency is a key aspect of managing a successful healthcare business. Predictive modeling allows healthcare practices to identify patients who require more urgent care than others, leading to a more efficient workflow. Additionally, these data analytics can anticipate the likelihood of future events, for example, seasonal flu outbreaks, and consequently alter the medical staff scheduling.
Every healthcare startup is looking for a way to reduce costs, and with predictive modeling on your side, you can be well on your way to saving money. More effective care based on genuine patient needs can cut down on readmissions and length of stay without negatively impacting health outcomes. The predictions that are elicited can also help healthcare businesses allocate their funds and resources more appropriately, ensuring that purchases are necessary and beneficial.
Perhaps most importantly, predictive modeling leads to better patient care outcomes. The use of a data warehouse in healthcare means that staff is better informed about the needs of their patients. They can ascertain diagnoses faster, pick up on allergies, medication, and treatment history and identify risks before the patient’s health reaches a critical stage.
So what exactly does predictive modeling in healthcare look like? Whilst this will vary depending on what field your practice specializes in, we’ve compiled a list of some of the most common current uses of predictive modeling in healthcare.
Optimizing an efficient schedule is necessary for a healthcare business to operate smoothly. However, even the most experienced practice will have days when their schedule is interrupted or delayed by no-shows, emergency appointments or urgent meetings. Predictive modeling can identify patients who frequently miss appointments or are often late, leading to a more efficient schedule and reducing wait times.
Regulatory compliance in healthcare can be difficult to manage, particularly given the frequency with which cyber attacks occur. Deciding to use predictive modeling software will help protect your patient data, as the system can track users in real-time and identify any suspicious activity.
Readmission rates are one of the most commonly used tools to determine the quality of care. As such, it is always in the best interest of a healthcare business (and their patients!) to reduce readmission. Predictive modeling has been shown to drastically reduce this number by identifying the factors that contribute to a high-risk patient. This information allows practitioners to preventatively target these concerns and reduce the likelihood of readmission
Predictive modeling software can identify patterns or unusual aspects of images, including X-rays. Automating this process not only saves valuable time, but also allows healthcare practices to ascertain diagnoses faster and determine the patients that most require care.
Every aspect of the healthcare process, including check-in, appointment scheduling, diagnosis, and treatment, can be improved with the use of predictive modeling. Finding a way to optimize these aspects of care without compromising the health of a patient not only improves treatment outcomes but also allows you to overcome revenue challenges.
Being able to detect patients who are at risk of self-harm or suicide is key to preventing these risks. Predictive modeling analyzes data pertaining to an individual’s health and has been repeatedly shown to accurately identify those at risk of both self-harm and suicide. Practitioners can respond to these cases appropriately and help protect the health and safety of their patients.
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In relation to implementing predictive analytics in healthcare, there are a few things you should be aware of.
Continuous advancements in technology have occasionally led doctors to be skeptical. This is an entirely understandable stance, as at the end of the day, machines can’t provide the same kind of care as medical professionals. Nevertheless, the advantages posited by predictive modeling shouldn’t be ignored, and it is in the best interests of both patients and doctors to utilize these systems. The best way to ensure practitioner approval is to involve them in the development of software, so it can accurately target business needs.
The importance of healthcare data security also affects predictive modeling. Prior to implementing any software, your practice must conduct research into its security measures. It should be HIPAA-compliant and have strict privacy controls to ensure the safety of patient data. Additionally, healthcare practitioners must be aware of the limitations of this software - whilst it is greatly beneficial, it should never be used as a replacement for real-life care.
There are currently limited regulations in place concerning the development of predictive analysis, which means that bias in the algorithms is occasionally present. Whilst this is hard for a healthcare practice to mitigate, you should do your research into the reliability of different vendors and ensure the vendor you select is willing to hear feedback and adjust their algorithms when necessary.
The increased use of predictive modeling is one of the biggest healthcare technology trends that we are currently seeing. This change is incredibly exciting as it opens up an immeasurable number of opportunities for both patients and doctors. As more and more patients are looking for healthcare businesses that offer innovative solutions, implementing predictive modeling is a fantastic marketing strategy that can help improve your revenue. More importantly, however, this type of software will allow you to optimize your business processes without sacrificing the health of your patients - it’s literally a win-win!
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