Data Science
in Healthcare

Disease surveillance involves the perception of the disease, understanding its condition, etiology (what causes it) and prevention. Information obtained with the help of Electronic Health Records (EHRs) and the Internet has huge potential for disease analysis.

Data extracted from these sources can help in planning of services, effective treatment strategy and development of health policy.

One concrete example of how we can use data science to study disease better is image processing (image analysis). It offers valuable knowledge about anatomy and organ functioning, e.g. when using anomaly detection algorithms (based on machine learning algorithms).

Using advanced analytics will enhance screening, diagnosis, and prognosis of diseases. In the future, integrating medical images with other types of information such as genetic data (based on genetic research in genetic sequencing and mutations) will increase accuracy and facilitate early diagnosis of acute and chronic diseases.

Data science is also revolutionizing the way healthcare is administered. By utilizing data mining warehousing techniques, we can now store large amounts of data in the cloud. Researchers are able to gain valuable insights from it when considering ways to improve healthcare systems.

Additionally, by using natural language processing (NLP), providers can better navigate and understand electronic medical records. An example of this is translating language from imaging reports into structured, searchable data.

Sentiment analysis (using NLP to detect the emotional tone of text) of online patient feedback can also predict healthcare quality and provide a cost-effective alternative to traditional surveys.

Lastly, medical data science is being used to optimize clinical schedules and cut costs by reducing unnecessary testing and predicting patient no-shows.

Data analytics can play a crucial role in protecting the privacy of patients and detecting fraud in healthcare, too.

Researchers have invented various ways to ensure security and maintain the anonymity of patient data. These include unique anonymization algorithms that work for both distributed and centralized anonymization, as well as cloud-computing frameworks for storing EHRs.

Additionally, big data analytics tools such as Google's big query are used to perform analytics on large datasets. They provide better data protection compared to traditional data analysis techniques, which have weaker anonymization.

All this is especially important in light of the growing usage of social media. Many people place personal information there. Because of this, existing preventive measures for anonymizing data are becoming less effective.

Mental health is just another field where data science can help doctors to provide better care. For example, a group of researchers recently developed a data analysis-focused treatment technique to help doctors manage patients with anxiety disorders.

Their model used static information such as age and sex, and dynamic information like context of stress, climate, and symptoms to construct static and dynamic information based on user models.

The reason this is beneficial is that data context seems to be more important than data volume when it comes to mental health. Techniques that are tailor-made for their field, like in this example, will help improve the diagnosis and treatment of patients.

Detection and response to disease outbreaks are also heavily influenced by data science nowadays. We just need to look at the (still ongoing) pandemic to realize how important healthcare data science has been in fighting COVID-19.

Using analytical modes, scientists were able to predict infection waves and hospitalizations. This information was essential to prepare healthcare systems for what was to come and to implement appropriate restrictions to lower infection risk, such as social distancing.

Pharmacovigilance involves tracking and identifying adverse drug reactions (ADRs) after a drug's launch to ensure patient safety. Data mining techniques can be used to analyze adverse event reports (AERs) and identify a potential drug response during clinical trials.

They even increase the detection rate by up to 31% when used in conjunction with electronic health records (EHRs). Furthermore, pharmaceutical companies can utilize data science to help avoid false alarms when identifying potential ADRs.

Data science can be used in medical imaging by utilizing predictive analytics to analyze large amounts of medical imaging data, such as scanned images of the human body, to generate accurate predictions and insights. Healthcare data scientists can accomplish this by using descriptive image recognition algorithms, which can analyze patterns and features within the images. By utilizing predictive models, trained on the collected data, healthcare providers can make predictions about the diagnosis or treatment of a condition.