What is healthcare data analytics?
Healthcare data analytics is gathering data using qualitative and quantitative methods to derive insights and trends within the healthcare process that improve the overall function of healthcare organizations. Healthcare data analytics enhance diagnosis, improve patient management, understand disease spread, and better manage clinical procedures and workloads. Historical and real-time data are gathered and extracted from healthcare IT systems to analyze medical files, patient records, and test results to help improve medical practices, organizational workflows, and patient engagement.
Healthcare data analytics leverage predictive analysis, statistical modeling, data visualization, cluster analysis, complex event processing, forecasting, artificial intelligence (AI), machine learning (ML), and process automation. These generate a deeper understanding of data stored in and across healthcare technology systems. Healthcare organizations that invest in data analytics and develop an organized approach to data modeling can improve decision-making, adapt to market changes, optimize healthcare services, and advance patient health outcomes. Healthcare organizations gain a holistic understanding of clinical administration, operations, and services with the right data analytics strategy that cleanses and standardizes data sets.
Data analysts gather health data stored within health systems, information systems, and external applications and use operative methods that transform the data into understandable and presentable ways, i.e., graphs, tables, and illustrations. The organized data can optimize healthcare treatments, assist in clinical decision-making, investigate health trends, simplify business operations, and better understand patient health profiles. Data analysts work in healthcare, technology, and government sectors to identify and present data to improve health management strategies.
How can healthcare data analytics be applied to improve health outcomes?
Data insights from healthcare analytics can improve care protocols, management practices, and patient care delivery. Examples include:
- Improving mental health diagnosis: Clinical operators use machine learning algorithms to improve the screening process used to test and verify mental health disorders. Analytics improve the questions and terminology that best represent the mental makeup, emotional state, and personality wiring of patients with mental health issues, increasing the rate of accurate diagnosis.
- Automating image readings: Machine learning tools scan, assess quickly, and determine test results from X-rays, scans, and ultrasounds. Advanced analytics powered by artificial intelligence (AI) can rapidly read and compare a patient’s test results to previous screenings, improving the odds of determining initial symptoms of cancer or disease progression.
- Streamlining COVID-19 tests: Hospitals can utilize AI platforms to speed up manual screening procedures to deliver faster results to patients waiting for appropriate medical instructions. Automated COVID-19 screening and test results enhance clinical operations by helping patients quickly receive hospital care or begin isolation, slowing disease spread and improving patient health outcomes.
- Assessing prescription methodologies: Physicians and clinical care centers harness advanced analytics to provide insights into patient medication history. Insights can be used to determine if a patient has developed an over-reliance on medications that do not directly mitigate symptoms related to states of disease or chronic conditions, i.e., opioid addiction.
- Measuring physician performance: Healthcare data analytics extract raw data from healthcare IT systems and generate a statistical analysis of patient feedback data, helping physicians to understand patient preferences better and apply improved methods of medical care.
What are the benefits of healthcare data analytics?
- Quicker access to patient data: Healthcare data analytics rapidly unlock the patient medical history and can provide insights that inform physicians on hard-to-uncover patterns, leading to better treatment and care outcomes.
- Actionable insights: AI, machine learning, pattern matching, cluster analysis, and neural networks provide actionable insights that lead to increased knowledge of symptom management, chronic care methods, and quality of treatment outcomes.
- Public health management: Healthcare organizations can use data analytics to gather information from various health management systems and analyze the data to predict a rise in a disease outbreak, preventing public health crises.
- Patient engagement: Hospitals, health clinics, and physician offices create better patient engagement with deep insights from high-quality data on patient records and files. To initiate personalized healthcare, data managers harvest patient data using AI, graph analysis, multivariate statistics, and data mining to create statistical models on patient activity and behaviors.
- Increase market share: Hospital organizations, provider networks, and healthcare groups can increase market share by using data mining methods to find relevant information about consumer behavior patterns. Insights can reveal popular healthcare programs and treatments, helping to build stronger consumer loyalty.