The healthcare sector is constantly evolving and, consequently, is impacted by technologies. Data analysis is a process of evaluating information from various sources, structured or unstructured, using multiple methods. The procedures aim to identify trends and obtain insights that would otherwise remain hidden.
The collection, processing, and storage of these assets, which provide business intelligence, bring with them several benefits. Therefore, all professionals can benefit from using data analytics techniques in medicine, while always remembering to comply with General Data Protection Law regulations in healthcare.
In this article, we will show the advantages of using data analysis in medicine and explain its different applications. Would you like to understand what changes can be made through strategic data analysis? Keep reading!
Understand the various uses of data analysis in different segments.
Hospitals, clinics, and medical offices generate a vast amount of data. Therefore, technological solutions and the work of professionals are fundamental to finding new strategies to increase operational efficiency and improve outcomes for both institutions and patients. See below how the data analytics services in this information are useful in the healthcare field:
More effective sharing of patient data;
- Automation of bureaucratic and repetitive tasks;
- research and prediction of diseases;
- Accurate predictions based on real-time data;
- Automation of hospital administrative processes;
- Accessibility to information, which can be accessed by the public;
- early detection of diseases;
- Faster responses to changes in the healthcare market;
- prevention of unnecessary medical consultations;
- improving collaboration between organizations in the field;
- discovery of new drugs;
- Easier sharing of health data between partners and colleagues;
- More accurate calculation of health insurance rates;
- Personalizing medical care for the patient ( Precision Medicine ).
There are programs that identify, validate, and evaluate data to provide answers to specific research questions. The software captures information from sources such as case files and patient surveys. Furthermore, it performs assessments to suggest actions aimed at achieving health goals and ensuring that patients benefit.
Discover the different applications of data analysis in medicine.
Patient health data sources typically include electronic health records , medical images, examinations, sensors, and computerized systems. This information is combined using various techniques to generate reports. Below are key applications of big data analytics that answer questions in medical institutions!
1. Descriptive analyses
It is possible to obtain information about past occurrences through descriptive analyses that use historical data to discover patterns and make comparisons. Therefore, these assessments serve to answer questions about what has already happened in order to find new methods for disease prevention , improve the delivery of clinical care, and measure the effectiveness of treatments.
Data can be collected from clinical notes, laboratory reports, advanced disease research, radiological and pathological images. Data analytics processes provide physicians with various tools that help them personalize their patients’ treatments and show a clearer clinical context for each diagnosis.
2. Predictive analytics
The results of this type of assessment are appropriate for answering questions about what might happen next. Predictive analytics uses historical and current information to make predictions about trends. Healthcare professionals can gain insights to improve patient care ( “healthcare consumers” ) and to manage procedures more effectively.
On the other hand, healthcare professionals can use predictive analytics to identify high-risk patients and prevent potential illnesses. Various mechanisms use natural language processing to evaluate unstructured data in medical records, and to verify factors inherent to people’s lifestyles.
3. Prescriptive analyses
Machine learning is a relevant factor in these assessments. Prescriptive analytics assists in projecting future outcomes , and the information gathered can point to the best course of action for each strategy. Therefore, they integrate and interpret each piece of information, reveal solutions, help create plans, and validate the answers found.
The results enable the discovery of more effective medications and treatments that positively transform healthcare services. With technological solutions, the continuous growth in the amount of available data and the application of advanced prescriptive analysis techniques, it will be easier to achieve improvement and precision in all areas of healthcare.
Discover which types of indicators are monitored in Medicine.
Medical indicators facilitate the evaluation of processes and patient health conditions. They can be based on averages or percentages that provide a quantitative basis for achieving improvements in the quality of services. Metrics can also identify incidents that require further investigation. Check out more details on the topic!
4. Clinical indicators
These indicators facilitate the monitoring of the quality of medical care provided to the public. They create a basis for improving health systems and reveal reliable clinical data. For this reason, they need to be defined and implemented with appropriate scientific knowledge, as they can reduce complaints from patients treated in medical organizations.
The quality of patient care depends on documentation of how serious illnesses are treated and systematic evaluation of the results of resources used in specific circumstances. Clinical indicators make it easier to measure service performance and the care provided to patients. This is because hospital managers can conduct consistent analyses based on evidence and care standards.
Clinical indicators demonstrate the level of excellence of the institution , increase the likelihood of achieving desired results, and are consistent due to the application of professional healthcare knowledge. These metrics can be divided into different dimensions according to the characteristics of the care being evaluated. They are measures that make it possible to assess processes and outcomes.
5. Quantitative indicators
Quantitative measures can be used to assess and monitor the quality of important management, governance , and support functions that affect patient outcomes. They are valuable measurement tools or useful indicators that serve as guides for monitoring, analyzing, and improving the quality of clinical support services or other organizational functions.
The use of these metrics serves many purposes. Quantitative indicators also provide a quantitative basis for physicians, surgeons, and healthcare managers to achieve improvements in internal procedures. They allow for comparisons over time between different locations, making accurate judgments and setting priorities.
Quantitative indicators do not directly measure the quality of services, but they are used to select surgeries, support professional accountability, organize clinical care, and provide quality improvements.
The use of these metrics allows organizations to monitor and evaluate everything that happens with their patients in order to understand the consequences of the actions of professionals and organizational systems.
6. Scientific indicators
There are scientific indicators based on care standards, evidence, and academic literature —for example, research summaries, clinical trials, and meta-analyses. Some are extremely relevant when scientific evidence is lacking, as they are identified and determined by a group of healthcare experts who reach a consensus based on their experience.
Scientific metrics are reliable and valid, given that they relate to clearly identifiable and previously tested hypotheses. These indicators can be described according to the strength of scientific evidence to predict logical results. They are perfect when described exhaustively and uniquely and based on definitions predetermined by specialized professionals to detect false positives or negatives.
Therefore, these metrics are relevant to clinical practice , enabling useful comparisons based on scientifically proven evidence. Each scientific indicator needs to be defined in detail, with explicit data specifications to be specific. However, these indicators may have variations regarding their reliability or validity when used to analyze quality issues.
7. Generic indicators
Generic indicators are used to analyze chronic or acute diseases, as well as to apply preventive care. They are typically applied to discover diagnoses, treatments, or during screening. They encompass interventions, medication use, laboratory studies, examinations, and medical histories. They can be expressed as rates or ratios over a given period.
Generic metrics use average values from a population sample . With them, managers make comparisons between trends over time to specifically enumerate and name the population at risk of suffering from illnesses. In this way, they are indicators that point to intrinsically undesirable individual or collective phenomena, capable of triggering further investigations and analyses.
Generic indicators have low performance, but they are important for risk management. For example, they can be applied to analyze wound infections, where the denominator is the number of patients who underwent surgery and the postoperative hospitalization time is greater than or equal to a certain number of days.
8. Structural indicators
Some indicators relate to the processes, structures, and outcomes of healthcare. Structural metrics denote the attributes of the medical environments where procedures are performed , including material and human resources, equipment, facilities, and much more. They also relate to staff qualifications, medical teams, process organization, and the use of technologies.
Structural indicators may include averages related to specific units, the ratio of specialists to general practitioners on the medical staffing scale , and clinical guidelines that require annual review. These measures indicate the proportion of patients assessed by physicians within periods or hours of referrals, or the number of people referred for regular care, for example.
These metrics also record the number of patients treated according to clinical guidelines and outcomes for certain diseases . Based on this, doctors obtain data on morbidity, mortality, quality of life, health status, public satisfaction, patient profiles, etc. Professionals can visualize, in numbers, the consequences of their activities in interacting with patients.
Understand what changes can be made based on data analysis.
Data collection works if you know what to do with that information. Professionals need to find a systematic and centralized way to collect, store, and analyze each piece of information collected so they can use it to their advantage. Below, we will explain the changes that hospitals and other medical institutions can make based on their analyses.
1. Personalizing patient care
Data analytics makes healthcare delivery more efficient and, as a result, reduces costs. The collection of this information has been simplified in recent years and goes beyond helping to improve day-to-day operations . It allows for personalized patient care to make improvements, forecasts, and track trends.
2. Optimization of diagnoses and treatments
With data analysis, it becomes more practical to promote preventive measures, giving patients a clearer view of their health and treatment goals. Assessments optimize diagnoses and treatments, providing the speed and accuracy necessary for the healing process. This is relevant because the population seeks patient-centered medical care .
There is a growing demand for value-based care, and as a result, there has been a considerable shift in the focus of preventative measures capable of improving public health . Data analytics makes this possible, and instead of simply treating or alleviating symptoms as they appear, doctors are preventing the development of chronic diseases.
Advanced data analysis techniques in medicine accelerate the discovery of cancer cures and identify treatments used worldwide with the highest success rates. The databases that doctors access support their decision-making, making it more accurate, and are often derived from complex medical histories or patients suffering from multiple conditions.
3. Improved patient and employee experience.
The use of data analytics can support clinical treatment decisions made by healthcare professionals. These technologies provide more detailed individual patient information for hospitalization when no other option is available. They help predict risks of deterioration, infection, and hospitalization, as well as cut unnecessary expenses.
Databases have proven useful in treating patients suffering from various conditions. Artificial Intelligence (AI)-based analyses allow scan images to be read by algorithms that can more accurately identify potential patterns to arrive at diagnoses . The assessments help identify patients with depression or at risk of suicide attempts.
Managing healthcare-related information has the potential to lead to better care if done correctly. With centralized datasets, access to data is immediate, wherever and whenever needed . The addition of big data analytics also enhances efficiency across the board.
4. Maximizing resources
Technological solutions integrate data from devices from various sources provided by the patients themselves. The technology sends real-time alerts to doctors, who can access the information from anywhere, at any time. With current resources, healthcare professionals can predict disease outbreaks and prevent problems on a global scale.
Predictive analytics models, for example, use data mining, statistics, and machine learning to find patterns and predict consequences. These technologies are already being built and tested with real health data, collected from society to offer solutions at both micro and macro levels.
Health data collected by advanced technologies are applied to risk scoring, prediction of health deterioration, and much more at individualized patient levels. Predictive modeling can also be used on a much larger scale for population management in the face of pandemics and epidemics that can and should be controlled.
5. Reductions in healthcare costs
Healthcare is expensive for both the government and individuals using private plans. These costs tend to increase with age and population growth, a global reality. Through the use of predictive and prescriptive analytics, healthcare organizations can obtain detailed models to reduce risks and expenses .
Patient costs are also reduced through data analysis. Beyond patient-centric benefits, health assessments and metrics can decrease no-shows and missed appointments. Technologies help prevent equipment breakdowns, manage supply chain expenses , and reduce fraud.
Analysts observe the information being collected and use their skills to manage quality, analyze, and interpret data to provide physicians with actionable insights. The growing need for improvement in the healthcare sector has significantly increased the demand for qualified professionals who understand and know how to make choices.
6. Differentiated functions
Today, physicians need to understand how health information technologies (IT) and other systems in their field are developed and evaluated. Major technological advancements influence their activities and routines, leading them to assess raw data and examine updated and historical information. Solutions mine data and automate reports, but their use requires distinct skills.
There are professionals who work building predictive models and solving problems for the organizations that hire them. They do this because they are versed in several essential skills, for example, data visualization and interpretation, statistical programming, and Structured Query Language (SQL). They also develop interpersonal skills as healthcare data analysts.
To work in this field, you need training, good communication skills, attention to detail, creative and analytical thinking. A bachelor’s degree in computer science, statistics, mathematics, or data science is not a requirement to hold the position of health data analyst. However, in-depth knowledge in these fields is crucial.
6. Market opportunities
A postgraduate degree can qualify professionals for higher and more competitive positions. You can obtain your certificate if you want to work with data analysis and already have a completed undergraduate degree. Many organizations use health information and require the help of those with qualifications and expertise to work in diagnostic centers .
There are also many opportunities in government health departments, hospitals (private or public), medical offices, companies that sell health plans or provide health consulting services, IT providers, etc. Depending on the sector you choose, you can even work as part of teams or independently, if you prefer. The financial gains are quite promising for those who invest in this area.
Learn why it’s important to stay updated on the topic.
It is essential to learn about how data analysis is performed in medicine, especially if you intend to work in clinical or hospital settings. Future administrators, executives, doctors, and researchers will succeed in their careers if they understand the benefits that data analytics solutions can offer to patients in general.
1. Use of advanced techniques
Many healthcare professionals are already working alongside individuals trained in advanced data analysis techniques. Doctors can also work directly with new tools that feature graphical interfaces, even if they lack experience using Big Data. All that’s needed is responsibility and a willingness to learn new things.
2. Agility in clinical practices
In healthcare facilities, data analysis can reduce patient wait times by improving scheduling and training staff. Metrics guide healthcare providers to offer patients more treatment options, schedule appointments, and reduce hospitalization rates. More efficient clinical practices reveal which individuals are at higher risk.
3. Prediction and prevention of treatments
Data analysis helps healthcare managers work towards predicting and preventing illnesses instead of focusing solely on treatment responses. Predictive assessments are crucial for identifying patients at higher risk of chronic diseases. They are based on social and laboratory factors.
Did you enjoy learning more about data analysis in medicine?
Data analysis in medicine is being increasingly encouraged through a data-driven culture . And its benefits extend to everyone in the healthcare chain, from patients to hospital managers.