Exploring health sciences through data analytics involves using available real-time information to gain insights, make informed decisions, and improve outcomes in the healthcare domain. We live in the era of modern data sciences, artificial intelligence (AI) and machine learning. Hence, it is timely needed to utilise data analytics in health sciences to get advanced knowledge for better implementation decisions. Here’s a step-by-step guide on how to approach this exploration.
Lahore-UBAS offers various study programmes for biology and applied sciences domain. Therefore, this article will upgrade your perspective regarding the benefits and usage of data and data analytics in health sciences to make informed decisions in the life-concerning field.
Define Objectives and Research Questions
Clearly define the goals of your exploration or research. What specific aspects of health sciences do you want to analyse or improve through data analytics? Formulate research questions that guide your analysis. In medical or health sciences, research is an integral part of progress. The same is the case with data science research.
Similarly, it is essential to set objectives and research question formulations when using data analytics in the health sciences domain.
Identify Relevant Data Sources
The next step is the identification of reliable data sources. It is always best to check the source of information to have confidence in its data and consequential results. Hence, it is always advised to determine the data types relevant to your research objectives. Data sources can include:
- Electronic Health Records,
- Clinical Trial Data,
- Public Health Databases,
- Genomic Data, and more.
Data Collection and Integration
In the next step, gather the necessary data from diverse sources. Ensure that the data collected is of high quality, and if needed, integrate data from vast and multi-layered sources to create a comprehensive dataset.
If data collection from multiple sources is possible in health sciences and health care. It will enhance the credibility of available data. In healthcare, different tools are used for data collection, cleaning, and analysis. Furthermore, primary data is collected from direct data collection and through books and published research; it is called a secondary data source.
Data Cleaning and Preprocessing
Once you collect data from primary or secondary sources, cleaning the data by handling missing values, removing outliers, and addressing any inconsistencies for ready-to-use data is essential. Preprocess the data to make it suitable for data analysis, including normalisation and transformation as needed.
The primary data here converts into secondary data sources once processed. It stays safe in collective data analytics. Furthermore, for doctors and medical practitioners, it becomes very convenient to trace back the medical history of any patient at any time of need.
Exploratory Data Analysis (EDA)
Once your data is ready, you can conduct exploratory data analysis to understand the patterns, trends, and relationships within the data. Visualisation techniques can be valuable in gaining insights and identifying potential areas for further investigation.
Through this method, you can get real-time data regarding the various aspects of any subject of health sciences by processing the data analytics. This technique is equally suitable for various applied sciences.
Hypothesis Testing and Statistical Analysis
Now, test the research question you have drafted in the initial stages. Formulate hypotheses based on your research objectives and conduct statistical analyses to test these hypotheses. Determine the significance of observed patterns or relationships.
In this way, you can draft decisions and gather facts. This will lead you to add up proven theories or rejected statements or myths related to different health science lines and sublines. Hence, you can explore many arenas by using data analytics in health sciences.
Predictive Modelling and Machine Learning
Once the statistical results are drafted, it’s time to apply predictive modelling and machine learning algorithms to make predictions or classifications based on your data. This can include predicting patient outcomes, disease risk, or treatment effectiveness. In this way, you can use data analytics, data analytics algorithms and data analytics sources for a longer time. You can also testify to the progress and statements that may be new at any time.
Clinical Decision Support Systems
Health Sciences are very closely related to health care. Therefore, data analytics can also be applied and beneficial for the healthcare sector on national and international levels.
You can develop or integrate data analytics tools into clinical workflows to provide healthcare professionals with real-time information and decision support. This can help in personalised patient care and treatment planning.
In this way, the workflow would be smoother, and the chance of errors and omissions would be reduced to a greater extent.
Ethical Considerations and Data Security
Health sciences and healthcare are both highly sensitive. Hence, ensure compliance with ethical standards and data security protocols, especially when dealing with sensitive health information. Protect patient privacy and adhere to regulations such as Health Insurance Portability and Accountability Acts (abroad) or any other acts as the local or international laws are concerned.
Ongoing Analysis and Continuous Improvement
Data analytics in health sciences is often a continuous process. Therefore, when conducting research in health care or health sciences, it is important to regularly analyse and refine your models based on new data and insights. Stay informed about advancements in both data analytics and health sciences.
Remember, data and methods can both become obsolete quickly. The data analytics tools usually utilised in health care are three steps: collection, cleaning, and analysis.
Collaborate with Domain Experts
Ensure to work closely with healthcare professionals and domain experts to ensure that your data analytics approach aligns with clinical realities and contributes meaningfully to healthcare outcomes.
In a nutshell
By keeping these steps in mind, you can systematically explore health sciences through data analytics. It will help you contribute to advancements in healthcare research, patient care, and overall well-being. In Pakistan, all these things are often practised on manual and outdated procedures, which is both time-consuming and tiring. Furthermore, with data analytics in health sciences, we can work on individual and social improvement in the healthcare sector.