Data Analytics in healthcare allows use of new technologies both in treatment of patients and health management.
It is a system with varied stakeholders: patients, doctors, hospitals, pharmaceutical companies and healthcare decision-makers.
Data analytics is the process of analyzing raw data to find trends and answer questions, and identify trends, it includes many techniques with many different goals. By combining these components, a successful data analytics initiative will provide a clear picture of where you are, where you have been and where you should go.
There are four primary types of data analytics: Descriptive, Diagnostic, Predictive and Prescriptive analytics. Each type has a different goal and a different place in the data analysis process.
All of these four types of data analytics provide the insight that businesses need to make effective and efficient decisions.
- Descriptive analytics Can be used to determine how contagious a virus is by examining the rate of positive tests in a specific population over time.
- Diagnostic analytics are be used to diagnose a patient with a particular illness or injury based on the symptoms they’re experiencing.
- Predictive analytics forecasts the spread of a seasonal disease by examining case data from previous years.
- Prescriptive analytics assesses a patient’s pre-existing conditions, determine their risk for developing future conditions, and implement specific preventative treatment plans with that risk in mind.
Applications of Data Analytics
The increase in the number of patients has made it difficult for doctors and staff members to manage work efficiently. According to a recent analysis report, health-care expenses for the USA are in excess of 17% of GDP. With the rise in such needs, data analytics can serve as promising solution to solve issues in the healthcare industry. According to market analysis, the data analytics sector is expected to be more than $68 billion by 2024. The target healthcare sectors where data analytics can bring significant change include Drug Discovery, Disease Prevention, Diagnosis, Treatment, Hospital Operations.
Drug Discovery
Generally, the drug discovery process takes a long time, about 12 years, and costs way too much. Data analytics increases the drug delivery process rate in medical science, helping to gain faster approval in the Food and Drug Administration and curing patients faster. Quite a few companies are developing artificial intelligent machines for applications in various sectors.
Disease Prevention
Data analytics prevents diseases by early recognition of risks, and the tools also recommend preventive plans. Various smart devices using data analytics use people’s historical patterns to recognize issues before it gets out of hand.
Diagnosis and Treatment
Another useful application of data science in healthcare is medical imaging, wherein the algorithms efficiently interpret X-rays, MRIs, Mammographies and other types of images, which help in the identification of patterns in the data and detection of tumors, organ anomalies, artery stenosis clearer. Data analytics algorithm models can diagnose irregular heart rhythms from ECGs faster than a cardiologist and clearly distinguish between images of malignant lesions and benign skin marks.
Today’s treatment in healthcare has become more comfortable with the availability of more data on individual patient characteristics, enabling the delivery of more precise prescription data and personalized care. Data science is enhancing the emerging field of gene therapy. Inserting genetic material into cells and replacing traditional drugs is more manageable than before.
Hospital Operations
Data analytics help to enhance the workforce of the staff members at Hospitals by assigning them certain hours, ensuring enough hospital beds are available, enhancing utilization in the operating room. Another analytics tool, i.e., Predictive analytics, can optimize scheduling.
Prediction of Speed
Similar to the flu example is the coronavirus (COVID-19) pandemic. Analyzing data to predict future spikes in cases can help hospitals ensure staff have enough personal protective equipment and patient beds. It can also enable school administrators to decide whether in-person learning is a safe option, and individuals to make sound choices regarding personal safety and hygiene, social distancing, and travel.
Potential Dangers to Avoid
While data analytics has the power to drive positive change, it can create and perpetuate issues.
When handling patient data, keep in mind that it is sensitive, personally-identifiable information (PII). It must be protected at all costs. As a medical professional or administrator it’s your responsibility to keep patients’ information secure while improving their health and wellbeing.
It’s also important to catch and correct biases in algorithms when analyzing data. This is because when people write algorithms, they often display human bias.
Machine-learning algorithms learn based on the data they’re given. So we should ensure that data comes from a truly representative sample comprised of various demographics before drawing conclusions.
The future of data analytics in healthcare
Like every industry, the usage of data analysis in healthcare has its pros and cons. The data in hospitals and administrative units are generally in a desperate state. It is challenging to integrate data analytics into the healthcare system. Patients are concerned about the privacy and protection of their health information. There is no dearth of doubt that data science can solve the shortage of doctors. However, many people are concerned about losing the patient-doctor relation to computer algorithms. Nevertheless, the amalgamation of data science and healthcare will surely to grow in the coming years, not to mention it has come a long way already.
