A working healthcare system is vital for the survival of humankind. Without doctors, treatments, and disease prevention measures, we'd go back to the Dark Ages (and even then, there were doctors.) Modern healthcare equipment generates a lot of health data, and this is where Big Data applications can help.
The tools for big data analytics and data science for healthcare may vary, but the need drives the technologies to evolve. An intricate net of different databases covers every aspect of the industry - from logistics to the genome structure. Each such database contributes to medical services in its way, and every one of them requires data science tools to make the most of its contents.
In this article, we will explain why data science is essential for healthcare and cover some of the most prominent use cases.
Every healthcare workflow has several goals:
- To provide effective treatment with the risk factors minimized;
- To deliver medical services on time (i.e., when it is required and not after the fact).
- To be agile and adapt to any occurring scenarios (emergency or not).
These workflows depend on data to be useful: test results, previous researches, background checks, etc., which precisely where the adoption of advanced data science methodologies can help.
Data science makes healthcare more:
Let’s look in detail how Data Science makes the difference in Healthcare.
The purpose of the Healthcare Data Scientist is to make sense of all the incoming data and make the insights usable by the rest of their colleagues - researchers, doctors, and others.
Overall, the job of the Data Scientist in healthcare covers the following fields:
- Working with key business leaders to understand the needs and what kind of analytical data is necessary;
- Gathering incoming data;
- Structuring and synchronizing datasets;
- Providing contributions to Public Health Datasets;
- Performing database audits;
- Providing data analytics for various applications;
- Collaborating with ML and other developers teams to build the solutions;
Now that we have clarified what the Data Scientist's role is let's take a look at how the Healthcare system can benefit from data science.
To make sure that all the knowledge resources are readily available to people who are involved in health care, data management is an important task to take care of. Given the fact that the stakes are high, data processing should be meticulous to find out what is going on and what should be done. Another challenge is that the data should be continuously checked not to be outdated, incomplete, or inconsistent.
Data Science makes this process easier:
- The entire medical history of a patient can be compiled into one dataset (aka Electronic Health Record), stored in the data warehouse, and easily used for subsequent model training and testing.
- All information can be digitized, compiled, and distributed over numerous datasets and synchronized in case of changes, which solves the issue of piles of paperwork.
- Supplementary sources and additional research can help identify and fix the gaps in patient data.
- Cloud-based solutions scale, speed up, and streamline the processing of the data, giving the opportunity to, for example, get test results faster and spend less time deciding what treatment to use.
- Numerous internal and public health datasets offer medical information, which can be readily utilized by medical staff to figure out the correct course of action. Among the most prominent healthcare databases examples are - Healthdata.gov, World Health Organization datasets, data.gov, The Human Mortality Database and others.
- Machine learning algorithms help extract insights from information at hand, explore incoming data, and compare it to the available datasets to point out the best possible solution.
Another thing to consider is the Data Governance practices.
Since the healthcare industry is working closely with the patient’s personal data, it is essential to keep it safe and applied within the legal bounds (i.e., without disclosing any identifiable information by oversight or any other way).
Data governance practices also help to abide by the HIPAA – the Health Insurance Portability and Accountability Act of 1996, which requires pseudonymization of certain aspects of data and thorough monitoring of its use.
Therefore, Data Science applications enable better security practice and a more thorough audit of the system.
While medical personnel has to go through rigorous training and remember as much information as lawyers, there is always a chance that one needs more information to make the decision.
Correctly set up data management helps healthcare providers to be more efficient in their workflows because information is available and structured well.
Here’s how data science makes a difference:
- Databases and cloud computing features can drastically shorten the time required for the operation and increase the accuracy of the test results.
- Less time and precise test results lead to workflow efficiency growth. Basically, medical staff gets a chance to perform more tasks in less time (if necessary).
- Better efficiency leads to higher recovery rates, quicker emergency response and, most importantly, fewer fatal outcomes due to sepsis and other factors that require an immediate reaction.
- As a result, the patient experience and satisfaction are more favorable.
In addition to that, data science tools provide a better framework for the overall improvement of the healthcare system. Every test, scan, every prognosis, and treatment adds another case for the data processing algorithms, strengthening the analytical capacities of the global healthcare system.
Medical Image Analysis is one of the most exciting fields in pattern recognition technology. It is one of the critical elements in the examination and subsequent figuring out the treatment strategy.
Tests like magnetic resonance imaging (MRI), X-Ray, computed tomography, mammography, and others provide valuable and vital insights that can make a difference in the patient's treatment.
Image Analysis is also one of the fields where the accuracy of the image and its interpretation needs to be top notch. Advanced data science technology improves the process by providing more tools to handle such aspects as:
- Modality difference;
- The dimension of the images.
The process itself includes the following.
- Image processing algorithm handles the incoming images and helps to enhance, segment, and denoise them.
- Descriptive image recognition algorithm extracts the data from images, interprets it and puts them together into a bigger picture (for example, merging the images of the brain scan and designating them accordingly.)
- An anomaly detection algorithm looks specifically for the things that "stick out" on the pictures (bone fractures, etc.)
The databases and their extensive libraries with various examples are the backbones of the analysis. Incoming information is compared with the available datasets, and the gleaned insights give a better understanding of the patients' diagnosis.
Treatment personalization and forecasting are amongst the cutting edge fields in the modern healthcare industry. Deep insights into genetic structure and genomics provide doctors with a better understanding of what kind of treatment will be the best fitting for a particular patient.
The goals are simple:
- Understanding how DNA elements affect the health of the patient and his reaction to a particular treatment.
- Finding connections between genetics, diseases, and response to meds.
For example, DNA Nanopore Sequencer can detect pathogens that influence the drug response, which helps to choose the appropriate treatment and minimize the threat of sepsis occurrence. If we look at the technologies behind this solution, they are:
- MapReduce provides genetic sequences mapping which shortens the timeframe of the data processing operation.
- SQL is used to retrieve genomic data, BAM file manipulations, and provide the computations.
Being aware of what can occur throughout the treatment is the most significant benefit that data science and advanced use of databases bring to the fold.
Predictive analytics help to "foretell the future" and unlike magicians or random "prophets," the models are built on statistical information from available historical data collections that are continually improved.
The types of healthcare databases required for predictive analytics include:
- The patient’s medical history;
- The patient’s current condition stats;
- Clinical notes;
- Prescription databases;
- Genetic research;
- Drug-protein binding databases and so on.
A combination of data mining and machine learning can speed up the process:
- Data Mining provides more thorough datasets;
- The descriptive, exploratory and comparative algorithms can merge multiple perspectives into one and calculate the best match for a particular patient.
Predictive analytics can:
- Find the correlations;
- Find associations of symptoms;
- Find familiar antecedents;
- Explore the impact of biomedical factors (genome structure, clinical variable, et al.);
- Explore the effects of past and current diseases.
Using this information, predictive analytics algorithms can run models and forecast how this or that compound or treatment scenario will affect the patient.
The same process can be applied to specific elements of the process, for example, to estimate the timeframe doctors have for treatment before the patient's condition deteriorates.
Therefore, healthcare-related predictive analytics can be used for:
- Predicting the disease evolution
- Making disease progress prognosis, either symptom-based or timeframe-based
- Reducing the risk-factors and adverse outcomes.
Treating people and making decisions that can potentially save a person from a terrible disease is vitally important. Technologies have evolved, and it is great that such things as data science can help medical staff and clinical researchers make this world a better place.
Visualized predictive analytics turn complicated data insights into information that can help make decisions fast and this is critical in healthcare.
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