Analytics is revolutionizing healthcare by enhancing the capability of medical practitioners in providing effective diagnosis and medical treatment, enhanced personalized care, and better patient outcomes. Health diagnosis, currently, is mostly dependent on ailment symptoms and outcome of medical tests. This does not essentially highlight the health issues a person is susceptible to in future. Analytics grinds historical data and predicts potentially serious medical conditions, diseases, or infections, and predicts future wellness. Similarly, analytics helps identify the best treatment for a patient.
By leveraging analytics to integrate and analyze stored information like patients’ profiles, symptoms, findings, diagnosis, treatments, and medical history, physicians gain access to actionable data at the right time. This helps them ascertain the best possible treatment for patients and determine personalized care plans. Patients are well-informed about possible health issues in the future and take preventive actions on time to avoid or limit the severity.
Predictive modeling predicts not only the outcome of a medical operation or procedure considering the health conditions but also equips physicians with actionable data on what kind of problems could occur during the execution of the operation. Analytics also reduces life-threatening medical mistakes and health risks by warning the treating physician about the past and frequent mistakes in the undergoing treatment, thus helping them provide optimal care.
How predictive analytics reimagines healthcare
Predictive analytics uses advanced techniques to generate prediction models from past treatments. Healthcare providers make predictions by deploying these models on patients. A number of steps are involved in developing prediction models (See Figure 1):
Healthcare providers like hospitals, physicians, and nursing staff record all kinds of information about the patients including sex, age, location, work-life balance, standard of living, lifestyle, habits, health records following and preceding ailments, symptoms, medical test reports, treatment-related information like procedure details, prescribed medicines, diet plan, hospital stay, recovery time, etc. This information, when recorded in an organized manner, is a gold mine for the health industry.
Data Collection & Processing
For analysis, data is collected from diverse sources like databases, flat files, data marts, among others. One of the bottlenecks after data collection is its inconsistency. Data processing and consolidation, which includes data cleaning, validation, transformation, harmonization, missing value treatment etc., makes data ready to go for analysis.
Data Analysis & Insight Extraction
Once the data is collected, it is combined and aggregated using programming scripts at the desired level of granularity. It is analyzed from multiple dimensions to extract meaningful information. By analyzing patients’ data using exploratory data analysis, univariate analysis and multivariate analysis, deeper understanding of patients’ physical conditions, disease patterns, treatment requirements and other significant details are identified. These insights, reported in the form of various kinds of reports and dashboards, highlight the key facts.
Health Prediction with Analytics
A series of analytics models are developed by ingesting the patient’s health data. Predictive analytics uses a variety of statistical techniques like regression study, discriminant analysis, time series analysis, factor analysis, segmentation, text and sentimental analysis, and other machine learning and deep learning algorithms. Analytics answers many questions on future predictions and past events for a patient, for instance, what would be the success rate of a hernia operation, what is the risk of developing cancer in future, how is lifestyle affecting the patient’s health, etc.