Ginni Rometty, chairman, president and CEO of IBM
“In God we trust. All others must bring data.”
Serving people on such a large scale as is the case in today’s world, health institutes generate huge amounts of data. Clinical trials, electronic medical records, billing, wearable devices, CRM databases, and scientific articles provide so much valuable data every 10 seconds that it is impossible to process them efficiently without using advanced technologies and cutting-edge techniques. Data Science, a field of study that focuses on extracting meaningful insights from data, has found great use in today’s healthcare industry.
Big Data and Machine Learning in Data Science
The advent of Big Data and Machine Learning has made Healthcare more data-driven. The rapid pace of healthcare data growth is largely due to the emergence in the market of new smart software devices and solutions that enhance healthcare services. According to Cision, in 2018, global big data in the healthcare industry was valued at $ 14.7 billion and, by 2024, is forecasted to increase at a CAGR of around 20% to reach $ 42.8 billion. This will inevitably lead to further implementation of the Electronic Health Record (EHR) and increase healthcare spending.
SAS defines the term Big Data as “data that is so large, fast or complex that it’s difficult or impossible to process using traditional methods.” In order to tackle Big Data, data scientists resort to Machine Learning algorithms.
In the context of data, Machine Learning (ML) is a computer program that is able to parse data using specific algorithms. ML programs are able to modify themselves without human intervention, producing on the basis of analyzed data the desired output. In essence, by using ML techniques and analyzing huge amounts of data, the machine is “learning” to perform specific tasks.
Who Benefits from Data Science and Big Data in the Healthcare Industry?
The medicine and healthcare industry has been heavily utilizing data science in many of its segments, making them more effective and personalized.
Hospitals and Clinics reap benefits from data-driven insights, which optimize care efficiency, workflow effectiveness as well as the level of client personalization.
Insurance companies also take advantage of Data Science, implementing Big Data solutions on a large scale. Data-powered applications allow insurance providers to avoid fraudulent activities, rectify false claims, as well as provide more personalized services to their clients.
Patients can take the most out of data solutions and applications. Consistency of care, personalized medicine, accurate diagnoses, preventable care, and epidemic predictions are a small part of Data Science in Healthcare potential.
Data-powered analytics systems can help manufacturers to develop devices and applications that can effectively tackle customer health-related needs and problems.
Data Science can significantly enhance many processes of pharmaceutics: from the creation of drugs to the distribution of prescriptions. Being able to intelligently search vast data sets of scientific publications, patents, and clinical trials, data-based applications can accelerate the discovery of new drugs by enabling researchers to examine previous scientific materials and results of tests. Additionally, pharmaceutical companies can gain insight into marketing and sales performance for developing effective strategies for delivering the right medicine to the right market.
Some Promising Data Science Applications In Healthcare
Medical Image Analytics
One of the most revolutionary applications of Data Science is image analytics. In essence, medical image analytics is able to identify in scanned images even the smallest microscopic deformities. Industries that generate images on a large scale can significantly benefit from image analytics. With the advent of deep learning techniques in Data Science, by identifying patterns and detecting anomalies, software programs can learn to understand and interpret X-rays, mammographies, MRIs, etc. As a result, medical diagnoses become more accurate and exclusive of human error.
The software automates the process of bacteria detection, making it more efficient. Based on provided and processed datasets, the program recognizes specific stamps of bacteria and differentiates them in three different classes. As a result, MicroTechnix enhances the speed and efficiency of bacteria identification processes, avoiding human errors as the software is fully automated and does not need supervision.
Quantified Health is a relatively new concept, which refers to the processing and analyzing of data directly from consumer wearable devices. With the ever-increasing ownership of wearables, apps, and sensors that track heartbeat, temperature, sleep and step patterns, water consumption, or mood states, people are continuously creating a large set of health data. Cutting-edge technologies based on Data Science techniques and methods can help to analyze provided data in real time, detecting any deviations from the prescribed norms and reporting them to the users and their attending physicians.
Data Science for Post-Care Monitoring
There is always a risk of complications after any surgery or complex treatment. Once the patient leaves the hospital and is no longer under constant medical supervision, it becomes difficult to detect any anomalies or deviations in his/her condition in case they occur.
Remote post-care monitoring can help doctors to be constantly aware of the patient’s state of health. Using data-powered analytical systems, post-care monitoring software analyses collected data from wearable and home devices, supervises the patients’ health, and notifies their doctors in case of any complications.
Predictive Diagnosis and Disease Prevention
Data-powered predictive systems use historical data, learn from them, identify patterns and then make accurate predictions. In the healthcare context, such systems can process and analyze a patient’s past and current data, recognizing the risks and suggesting risk prevention plans. Tracking devices make it possible to detect a problem before it gets out of hand. The system identifies various correlations of symptoms, recognizes habits and risks of diseases, and makes meaningful predictions.
Data Science For Drug Discovery
Drug discovery is a very expensive and time-consuming process. According to Phrma’s report, the average cost of successful drug development is estimated to be $2.6 billion, and it takes at least 10 years to create an efficient drug and bring it to the market. Data Science and machine learning techniques can significantly enhance drug discovery processes. By extracting information from scientific papers, available patient data, and pharmaceutical reports, data-driven artificial intelligence software is able to identify relevant information faster and detect valuable correlations between biomedical entities, opening new research lines and enabling more competitive R&D strategies. Moreover, data science algorithms can simulate how the drug will act in the human body without long laboratory research.
NLP for Electronic Health Records
Electronic Health Records (EHRs) are medical data stored in digital format. Using the Natural Language Processing (NLP) technology, which is able to process and understand natural language, old unstructured hand-written records can be stored in electronic healthcare systems. NLP-powered systems are able to process unstructured data, analyze the grammatical structure, determine the meaning of the data, and summarize the information. As a result, hospitals and doctors can provide higher quality and safer care for patients.
Biometrics in Healthcare
Based on Computer Vision technology, Biometrics software scans particular body parts and matches them with available patterns. For instance, matching a patient’s face to a previous biometric sample. Mostly, hospitals use biometrics systems for patient identification at check-in in order to promptly get access to patient documents and records. According to Grand View Research, by 2025, the healthcare biometrics market is estimated to be $14.5 billion.
Moreover, with the recent advances in Data Science, biometrics can be used in diagnosing diseases. A biometrics system, which uses machine learning algorithms, can identify physical symptoms appearing on a patient skin using a phone’s camera. As a result, such a system exceeds doctor diagnoses in terms of speed and accuracy.
With the proliferation of Data Science and the emergence of numerous data-powered applications, the Healthcare industry stays at the forefront and actively implements data-driven solutions to provide its clients with a better lifestyle and personalized services. With the vast amount of data available in the healthcare market, including financial, clinical, R&D, administration, and operational data, data scientists can derive meaningful insights to enhance the efficiency of medical services and insurance providers, as well as pharmaceuticals.