Find out why advanced analytics is valuable for healthcare and how it differs from traditional analytics.
Every second, the healthcare industry generates a huge flow of data. RBC Capital Markets claims this is about thirty percent of all the world’s data. But it’s important not only to collect this data. «Raw» data is just a mishmash of zeros and ones. Technologies process it and extract meaningful information from it. This is what advanced data analytics software does. A healthcare organization can use this info to prevent diseases and treat patients effectively.
Forbes states that most healthcare industry stakeholders are ready to invest primarily in business intelligence, data analytics and insights.
Advanced Analytics vs. Traditional Analytics
While headlines may contrast advanced and traditional analytics, advanced analytics evolved from traditional. Previously, analytics was limited to descriptive and diagnostic functions. It used raw historical data to determine what happened (descriptive analytics). Traditional analytics then explained why it happened (diagnostic analytics). In other words, traditional analytics identified trends and patterns and found cause-and-effect relationships between them.
Advanced analytics enables healthcare organizations to access valuable insights proactively. It adds predictive and prescriptive functions to the above-mentioned. Predictive analytics takes existing data and forecasts events. Prescriptive analytics uses data to suggest specific actions needed to achieve a goal.
In addition to predictive analytics, advanced analytics includes:
- artificial intelligence and machine learning
- natural language processing
- deep learning
- data mining
Predictive Analytics
Predictive models that help healthcare organizations identify high-risk patients are the most common example of advanced analytics. Risk can be different: for example, the risk of a hospital-acquired infection or readmission. The study looked at two thousand five hundred eighty-nine isolated patients with coronary artery bypass grafting. About ten percent were readmitted to the hospital within thirty days of discharge. The analytics identified five risk factors. Based on this study, algorithms can predict readmission in other patients with few risk factors.
Specialists from the highly qualified Python development agency train the model to predict early signs of diseases, risks, etc. But training does not happen immediately. First of all, the developers clean the customer’s data sets, and then carefully study them. This is necessary to select the features most correlated with the predicted variable. Then the developers divide the dataset into test and training data
Artificial Intelligence (AI) and Machine Learning (ML)
Healthcare organizations have begun to apply artificial intelligence and machine learning in healthcare scenarios such as diagnostics and screening. Therefore, advanced analytics could be useful for improving care. For example, a deep convolutional neural network (CNN) demonstrated high accuracy and repeatability in screening and diagnosing retinopathy of prematurity. The algorithms outperformed human experts: ninety-one percent accuracy from CNNs versus eighty-two percent from experts.
A team of researchers from Johns Hopkins University created a new machine-learning approach: a mobile Parkinson’s disease scale. This scale objectively analyzes characteristics transmitted by a smartphone app. Among characteristics are gait activity, step length, reaction time, balance, finger tapping, etc. This technology complements standard Parkinson’s disease measures with objective, frequent, real-world assessments. They can improve clinical care and track how well therapeutics are working.
Natural Language Processing (NLP)
This machine-learning technology allows software to interpret, process, and understand human language. It’s beneficial for the healthcare industry. Many clinical and non-clinical companies receive info from different communication channels: instant messengers, social networks, mail, news feeds, and text messages. This info is presented in the form of text and voice data. NLP technology automatically processes this data and analyzes the mood or intentions of the sender in the message. It responds to human communication in real time.
NLP technology can be generalized and targeted. The first aims to present a broad picture of the patient’s condition or emotional state. It can be used for predictive modeling. Example: NLP algorithms identify patients at high risk for readmission. One of the effective tools for generalized NLP is Python NLP libraries (for example, SpaCy or NLTK). Belitsoft expert Dmitry Baraishuk emphasizes that Python libraries are used to train analytics models.
The second – targeted NLP – aims to identify a narrow set of concepts in the text. It can be used to answer specific operational or research questions. An example is a classification of medical files for fatal and nonfatal opioid poisonings.
Deep Learning (DL)
Deep learning is a part of machine learning. ML requires an engineer to adjust the algorithm manually. DL can learn from its mistakes. Knowledge is extracted from data sets automatically, using advanced methods that DL has found in artificial neural networks. It’s able to process unstructured data: images, documents, text, etc. DL tools detect genetic diseases by studying genes. Doctors can choose future drugs and treatments based on this data.
Deep learning is also useful when data on the daily activities and behavior of patients recording and collection is needed. For example, an acoustic sensing system is installed in a patient’s home. It recognizes and analyzes certain acoustic events in the patient’s daily life. DL and audio signal processing algorithms collect acoustic data about the patient and provide healthcare workers with remote, real-time continuous monitoring.
Data Mining (DM)
This technology sifts through large data sets and finds valuable insights and patterns. Data mining uses ML and statistical analysis techniques to turn big data into useful insights. Healthcare organizations need to make better clinical decisions. Hospitals are increasingly using clinical decision support systems (CDSS). These systems use machine learning to draw conclusions based on data analysis. For example, a hospital can compare patients’ symptoms and illnesses with similar cases or relevant clinical studies.
Doctors can also make diagnoses faster and more accurately. AI-based software can quickly analyze huge amounts of data (blood tests, X-rays, etc.). The clinicians make the final decision on the diagnosis, but analytics tips and well-structured data with insightful predictions can help them.
To End Off
Advanced analytics and big data improve and strengthen patient and provider relationships. Analytics tools can reduce the risk of readmission, predict diseases, and prevent epidemics. If a healthcare organization decides to invest in analytics solutions, it will be able to monitor and mitigate clinical risks and provide proactive care to patients.
