Abstract
The recent advancements in machine learning and natural language processing have helped us develop effective text analysis solutions. While the possible applications are still being explored, every industry is trying to leverage them to increase productivity and efficiency. Although, the healthcare industry has been somewhat slower in embracing the big data drive.
In 2011 alone, the US medical system has already accumulated 150 exabytes of medical/patient data. The US medical database is predicted to soon reach the zettabyte scale and so on. The size of the healthcare data available is mind-boggling. However, accessing and making sense of this data is unfortunately not so easy. Most of the data in medical records are unstructured and therefore difficult to analyze.
Text analysis solutions are based on NLP and machine learning and therefore can interpret and analyze massive volumes of unstructured text data with ease. Also, the various data analysis models can help you extract specific and relevant data as and when needed. This can be of great help in analyzing patients and predicting outcomes based on the outcomes of similar cases.
Introduction
The ability to leverage big data in healthcare has opened new possibilities and ways to increase the overall healthcare experience. Numerous healthcare providers are digitizing their operations. More and more healthcare systems are being integrated to monitor patients. Further, the introduction of various personal devices (ex: FitBits) to monitor health has started to flood the healthcare industry with patient data from various sources.
This data contains valuable information and insights about patients that can be leveraged to enhance patient care. Although, while the data contains valuable information, it is unstructured and difficult to analyze manually. It requires a sophisticated system or technology to make sense of the data.
Text analysis solutions can help you analyze extensive volumes of unstructured text data with ease. Not only can you reduce the time required to examine patients, but you can also predict the progression of their diseases or illness by accessing and examining data from previous similar cases. You can also evaluate risk and make better decisions to increase the success rate and healthcare experience.
A great example of a predictive healthcare application would be the Medical Home Network in the US. It is a non-profit organization that leverages predictive text analysis to identify seniors that are at risk of emergency transport in the next 30 days.
Predictive text analysis applications
Now that you have a better understanding of what predictive analysis is, let’s look at some of the most beneficial applications of text analysis.
Identify early signs of patient deterioration in ICU
Predictive text analysis can play a major role in monitoring and analyzing the health conditions of the patients, especially the ones in the intensive care unit (ICU). In many countries including the US, ICUs have been overtaxed due to aging populations. The conditions got much worse after the worldwide COVID-19 pandemic. The increasing need for complex surgical procedures and lack of ICUs along with healthcare specialists encouraged healthcare businesses to seek effective solutions such as text analysis to enhance patient care.
The rapidly rising adoption rate of IoMT (Internet of Medical Things) systems to monitor patient care has flooded the healthcare industry with a lot of patient data. Then there is the medical equipment to monitor the vital signs of the patients. All of this data can be leveraged to analyze the condition of patients. It can help you detect any or all signs of patient health deterioration so you can intervene at the right moment.
Predictive text analysis can also help you find early signs of adverse events among patients. Personal healthcare systems can provide you with the necessary insights and help detect early symptoms to provide the appropriate treatment. A hospital using this approach reported a 35% decrease in adverse events and an 85% decrease in cardiac arrest.
Predictive care for at-risk patients in their homes
Predictive analytics can help healthcare professionals stay one step ahead. Rather than a reactive approach, healthcare professionals can take on a proactive approach to enhancing patient care. While monitoring the patients in the hospital is important, so is taking care of the patients that have been discharged.
The elderly are at the highest risk of adverse effects or hospital readmission. These cases can be avoided by implementing appropriate preventive measures with the help of predictive text analysis. Using text analysis, you can analyze the patients’ entire medical history as well as his/her current vital signs. Based on the analysis of the medical records, you can identify the patients that are more likely to need further medical care.
One Medical Home Network in the US leveraged machine learning and text analysis to identify individuals with a heightened risk of developing severe medical complications due to COVID-19. This helped them better assess the risk and significantly reduced their required number of health care professionals.
Hospital equipment maintenance
The applications of text analysis thus far were entirely focused on providing the right medical care at the right time to avoid adverse effects. Although, predictive text analysis can do much more than that.
The aviation/airline industry has long been using predictive analysis to identify maintenance needs that will occur, in advance. This helps them replace mechanical parts before they go bad and put lives and at risk. The aviation industry uses sensors that transmit data from jet engines. Based on the analysis of the data, you can identify which parts need to be replaced 15-30 days earlier to avoid potential failures.
The healthcare industry can benefit from predictive analysis too. Components of medical equipment can go bad too, and you surely don’t want vital equipment failing at a crucial time. Medical equipment such as MRI scanners degrade over time, but monitoring their functionality with sensors and analyzing that data with text analysis can help you evaluate the condition of the equipment. By further analyzing the use of the machine for the coming days, you can schedule the maintenance when the machine is barely in use. This will help you avoid workflow disruptions and increase efficiency.
Diagnosis and treatment accuracy
The US medical system has been gathering patient data in the form of electronic medical records (EMRs). These EMRS contain a ton of patient data that can be used to analyze the progression of diseases. You can examine the symptoms and match them with the EMRs to identify similar cases. This can help you identify the disease as well as the rate of progression of the disease.
Similarly, depending on the insights from the past cases, you can choose the right treatment and medication. This can significantly reduce the time required to examining the patient and identifying the disease as well as the appropriate treatment.
Improve healthcare operations management
Text analysis solutions can help you improve healthcare operation management. The perfect area to start with would be optimizing patient to staff ratio so each patient gets the needed care and attention.
Although to optimize the staff, you need to predict the inflow of patients in the hospital. To do so, patients have to analyze data such as historical data, population, and demographic data, reportable disease, weather and sickness patterns, public holidays, data from nearby facilities, and more. Analyzing all of this data with the help of text analysis can help you predict the inflow of patients in hospitals at different locations.
Enhance cohort treatment
More and more countries are trying to build a digital database of patient medical records. These healthcare databases can be of great help in extracting insights into the health of the community. But access to this data is highly restricted to protect the patients.
However, the various drives towards open data for the common good of the community show great promise. More and more medical datasets are being made public to perform research as well as develop advanced solutions to enhance healthcare. These medical datasets can also be used to build profiles of communities and other cohort health patterns. This way you can understand which community is more affected by a certain disease. Governments and healthcare institutions can leverage this to identify communities that are most affected by obesity, or the ones that need to be made aware of smoking hazards.
Personal Medicine
It’s common knowledge that some medicines that work on a certain population group, might not work for another group. While all humans have a similar body structure and organs, they are much more unique and complex at the genetic level. But manually analyzing all this data is not possible for a single medical practitioner. Although, access to big data and predictive analysis can help you identify individuals that are more susceptible to a certain disease.
The University of Pennsylvania used big data and predictive analysis to identify patients most vulnerable to septic shock 12 hours before the condition surfaces. Similarly, hospitals can also use big data to identify patients that need immediate medical care and treatment.
For ex: Hospitals can analyze all data related to diabetic patients with text analysis. It can help you identify patients that are most vulnerable to the disease and need to be monitored consistently in the ICU.
Conclusion
Every industry is experiencing rapid technological advancements to increase productivity and efficiency. The healthcare industry is no exception, healthcare professionals are consistently exploring new technologies that can enhance overall patient care. Predictive text analysis already shows great potential and can become an integral part of the healthcare industry.
Understanding every facet of the patient condition, symptoms, treatment, and outcome is what predictive text analysis can help you with. But as informative as text analysis solutions are, the outcome eventually relies on the capabilities and experiences of the healthcare professionals. BytesView is an amazing text analysis solution that can help healthcare professionals uncover valuable insights from large volumes of data and enhance healthcare services
Shivam is a capable substance essayist who enthusiastically makes enlightening and intriguing articles. Shivam has a curious psyche and a hunger for learning. Shivam is a reality lover who loves to uncover captivating realities from a large number of subjects. He solidly accepts that learning is a deep-rooted excursion and he is continually looking for valuable chances to expand his insight and find new realities. So make a point to look at Shivam’s work for a brilliant perusing.
