Digital threats have been a growing concern for many businesses, especially as more industries embrace remote work. The number of ransomware attacks increased by 92.7% in 2021 compared to the previous year. What started as an uncommon malware for blackmailing smaller...
In healthcare, meeting the needs of patients is an ongoing challenge. Doctors, nurses, IT staff, clerks, janitors, equipment providers, and more must work together to provide the best patient care possible. With technology being a core component, predictive analytics has been a fast-growing industry. Due to its broad uses, it’s expected to grow annually by over 20% across the next five years.
Throughout the day-to-day functions, healthcare providers generate massive datasets. For example, the MRI of a human head takes over 20,000 pictures. Due to technology improvements requiring increasingly more data, interpreting it can be a complex task. There can be interactions between very different types of information. That has created a demand for newer methods, like predictive analytics, to convert data into a more usable form. In doing so, healthcare IT companies can help facilities make more accurate decisions for their patients.
What Is Predictive Analytics?
Predictive analytics is a type of analysis that uses artificial intelligence (AI) to help predict future events. It’s done with current and historical data, where it can support anyone from doctors to IT staff with healthcare-related decisions.
The datasets used are not limited to internal statistics either. Outside sources like local events, national holidays, weather changes, time of day, tourist traffic, etc., can have an impact too. These can predict many situations, like when extra staffing is needed or what types of health issues are more likely to happen.
Why It Can Improve Patient Care
The digital transformation of healthcare has created large amounts of valuable data. Electronic medical records (EMRs) are used by nearly 90% of office-based physicians and aren’t bound by the same limitations as paper records. With the help of AI-driven predictive analytics, that data can be looked at in new and innovative ways to improve the quality of patient care.
Increased diagnosis accuracy
One challenge for any doctor is a quick and accurate medical diagnosis. Patients will often have more than one underlying symptom, and it requires years of training and experience to judge. Predictive analytics doesn’t replace the doctor’s role but instead acts as a tool to support the process.
Medical providers try to decrease the chance of problems. Predictive software can detect patients that are at higher risk. Those risks can be observed through medical history and family genetics. By flagging people as being at increased risk for specific issues, lifestyle changes can be made earlier on.
Personalized care plans
Every person has unique lifestyle needs, dietary habits, chronic symptoms, and other factors that can impact their health. Data analytics can help develop personalized care plans, letting the doctor better identify areas of improvement while reducing the impact on other aspects of a patient’s life.
Reduced medical costs
More effective care means less wasted treatment and a reduced risk of an issue becoming more expensive. It can help in other ways too. Insurance plans can be developed with statistics from healthcare providers, creating ways to reduce overall costs by analyzing patient needs and trends.
Shorter wait times
It’s hard to anticipate surges, long appointments, or surgical complications. Walk-in clinics and ERs are especially vulnerable to a lack of schedule. That can leave patients waiting longer for treatment. Better predicting these factors can lead to shorter wait times through more accurate staffing.
Other Healthcare Uses
In addition to EMRs, many other data types are tracked and stored. That includes accounting, health insurance, cybersecurity, and other statistics like patient lifetime value. That allows predictive analytics to be useful for more than just patient care. It can also help with other healthcare-related areas.
Improved patient retention
Identifying areas and methods that patients are more responsive can improve care. Even if two people have similar health issues, they may be more responsive to different strategies. This not only enhances the quality of care but also decreases the chance of readmissions for the same problem.
Providers have a limited budget, staff, time, and medical supplies to meet patient needs. That can make it difficult to judge where best to put extra resources and where to trim down. Predictive analytics in healthcare will not only look at historical and current needs to better utilize resources but can help project future ones too.
Managing large data pools
When looking at a mixture of data pools, ranging from medical records to admission peaks, interpreting how they correlate can take a lot of time. AI-driven analytics can look at those interactions and more quickly present the information in a usable form. This makes it easier for healthcare staff to act upon it.
Cybercriminals are a constant threat to healthcare. They often target medical records which hold valuable patient data. When ransomware is used, those records are locked by a hacker, forcing the target to pay a fee to unlock the files. Data breaches can cost over $400 per patient record that’s stolen.
Using a combination of predictive analytics and standard security methods makes it easier to monitor for unknown threats. This is done by detecting unusual patterns in real-time and alerting IT staff. Even with cybersecurity built to handle the top IT problems, monitoring is vital to maintaining it.
From top to bottom, the healthcare industry uses data in various ways. It’s crucial for everything from medical records and treatment plans to admissions and cyber threat detection. This information can be better interpreted and used to support staff at all levels by using predictive analytics. Not only does it improve the quality of patient care, but it also helps providers get the most out of their budget and available resources.
As practical as it is, many offices aren’t prepared for the changes needed. That’s in part due to overhauls being a long and expensive process. It can include updating computers, software, networks, security, and training. Predictive analytics software must also be compatible with current systems, such as patient health records. With expert help, that process can go much more smoothly. Third-party healthcare IT companies like ITonDemand can assist by providing comprehensive and affordable services to meet your growing needs.
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