Technology is the now at the pivot point for all business or industry decision making; you have technology to move a business forward and you utilize the information extracted from collected data for a database or data warehouse. Every industry, whether it is manufacturing or sales, service-based or product-based, will tell you that it has a lot of data to process and that they are always looking for ways to in prove the analysis and discovery practices. The healthcare industry is no different except that there is usually much more data and much more complexity to that data than in just about any other industry. Yet, patients and professionals would benefit immensely from things like artificial intelligence and machine learning applications in healthcare.
Most other industries have been able to implement machine learning into their daily practices, and have seen increases to productivity, safety on the floor, profitability and structure. The reason that machine learning is becoming so popular – in true essence: necessary – is due to the vast quantity of data that businesses are handling. Much of the software that has been developed is adequate, but this isn’t enough any longer. The same state exists in healthcare, too, but the stakes are higher and more is expected from care received.
Machine Learning Application – Predicting the Future
We are all contributing to the big picture of healthcare and helping to find ways in which care can be provided more efficiently. This comes in the mandate of having all patient records in electronic form. By doing this, healthcare organizations are able to compare data, drill down to discover patterns and trends and also share information while keeping a patient’s personal details confidential. The more a healthcare system understands about patient trends, the better healthcare professionals are able to provide more accurate care, and predict who might need extra attention.
Hospitals must keep track of and report when a patient is readmitted into the hospital after a health episode, such as heart failure, pneumonia or stroke. The employment of machine learning in a hospital would be to identify which patients are at a high risk of needing critical care, usually within the first 30 days after being discharged. The machine systems looks at factors like:
- Medications the patient is taking
- New medications that were prescribed
- Lab results
- Treatments or surgeries received
- Vital signs while seeking treatment
- Personal and family history of illnesses
Armed with information like this, along with amassed healthcare specifics, a machine would not only be able to ascertain who has a greater possibility of readmittance, but also provide best actionable data for current procedures, which translates into better outcomes in the short- and long-term.
Machine Learning Application – Make the System More Efficient
No one likes sitting in the waiting room, whether it be at the doctor’s office or emergency room. Machine learning is looking to and already has implemented some means by which to alleviate healthcare inefficiencies. Things like distribution of medicines, staffing needs, and patient information delivery are easily managed without direct input from a person. Rather, a machine is able to learn from past models and take into account current and future needs, and from there produce a data-driven schedule that eliminates wasted time, energy, resources and manpower for that healthcare organization.
For example, if a doctor is able to dedicate his or her time to medical help and procedures instead of having to do research to find best practices or the newest techniques for a patient, that frees them to be available for more patients, answer more questions and provide more help. The machine takes some of the guess work out of treating a patient and delivers up the most accurate information in a timely manner.
Machines are also able to answer specific questions that a patient might ask by way of a patient portal or a healthcare organization’s online system. Most people’s concerns are easily answered, especially in taken in context with their health records. This allows a physician to turn over some simple duties to a machine learning system, and thus minimizes time spent either email with a patient or having to schedule that patient in the office. It is much easier to answer a few patient’s concerns or to click on a refill for prescriptions.
Machine Learning Application – Security and Privacy
Unfortunately, it doesn’t take much time or effort to find stories of when a healthcare organization has been hacked or when data has been compromised. Data security and protection is a game of catch-up to the assailants who are working tirelessly to find backdoors or weaknesses in a data system. With the real possibility of utilizing machine learning to safeguard a healthcare organization’s data, the number of cyber-attacks could be reduced or eliminated altogether. This would be directly due to the ability of a machine to analyze millions of points of data, figure out anomalies, and quickly determine if there is a threat or if access should be granted.
Healthcare now runs on the need for information to be shared across different organizations, across different departments, across the country and across the world in order to provide the most precise care possible. However, requests for data should take into consideration a patient’s demand for privacy; is the requesting entity someone that should have permission to retrieve the file or is it a malicious person looking for vital information? Differentiating, and doing so in a timely manner is exactly what the healthcare industry is needing.
Machine leaning applications in healthcare have only begun to be discovered, and developed specifically for this industry. As advancements are made in the fields of artificial intelligence and machine learning, you will see greater demand for its usage and broader ability in its usability in daily healthcare treatment. Machines are better able to tackle the exabytes of data being generated, decipher patterns, provide real-time data, and offer ways to improve efficiency both within the workings of an organization and delivery of care.