Healthcare in the 21st century poses many challenges, so any resource that can make the job easier is welcome and valuable. Enter data analytics.
This article explores the role of data analytics in healthcare. We’ll start with a review of what data analytics is, what types of healthcare analytics exist and how they’re used, the benefits of applying data analytics to healthcare, and a look at jobs and the future of health data analytics. We’ll also share an excellent online data analytics program you can take to improve your skills.
So, let’s start with that basic definition.
What is Data Analytics?
Data analytics is the process of changing raw data into actionable insights. The process includes many technologies, tools, and processes that rely on data to find trends and solve problems. Data analytics improves decision-making, shapes business processes, and fosters business growth.
Healthcare data analytics describes the analysis of health-related data employing quantitative and qualitative techniques to explore and find trends and patterns in the data. Health data analytics acquires, analyzes, manages, interprets, and changes data into accurate, consistent, and timely information.
What Are the Different Types of Healthcare Analytics?
There are four common types of health data analytics:
- Descriptive analytics, which uses historical patient data to gain insights into benchmarks and trends.
- Prescriptive analytics, which relies on machine learning to propose different strategies.
- Discovery analytics, which, like prescriptive analytics, uses machine learning. The difference, however, is that analytics employs machine learning to examine study data to determine patterns that could offer actionable insights.
- Predictive analytics in healthcare, which employs forecasting and modeling to predict what will most likely happen in the future.
The Advantages of Data Analytics in Healthcare
Health data analytics carries many advantages, particularly:
- Reducing costs. Anything that can help mitigate rising medical costs is a good thing. Health-related big data can potentially cut over half of healthcare waste costs by identifying trends and patterns that help healthcare providers find areas to streamline their operations and cut costs.
- Reducing human error. By analyzing EHRs and other data and looking for anomalies, analytics solutions can flag questionable entries for further inspection by healthcare professionals.
- Improving predictions and diagnostics. Artificial intelligence and machine learning can analyze healthcare provider data, predicting the most likely diagnoses for a patient’s condition. Additionally, AI and ML can suggest tests to confirm a diagnosis. Also, big data analytics can pinpoint patients at the most significant risk of suffering adverse healthcare outcomes.
- Increasing healthcare indicators. Big data analytics improves public healthcare indicators due to their effectiveness in use cases such as managing chronic diseases and diagnosing illnesses.
- Improving patient experiences. Data analytics enables healthcare providers to make smarter decisions in improving patient care and patient experiences. Big data analytics in healthcare gives patients better care for better outcomes and increases patient satisfaction when dealing with healthcare professionals.
Examples of Using Data Analytics in Healthcare
Here are two dozen examples of how professionals use data analytics in healthcare:
- Improved predictions for sufficient staff to handle patients. Hospitals can use big data analytics to forecast patient loads and determine how much staff is needed on any shift.
- Using Electronic Health Records (EHRs). Every patient has a digital medical record covering medical history, demographics, allergies, lab test results, etc. These records can be shared securely and available to public and private sector providers. Every record consists of an adjustable file so doctors can implement changes over time without inefficient paperwork and with no risk of data replication.
- Instant care real-time alerting. Wearables can continuously collect patients’ health data and send this data to the cloud, reducing or eliminating the need for the patient to be monitored in the hospital.
- Enhanced patient engagement. Thanks to wearables and trackable data, patients can be involved in monitoring their health. This technology, coupled with incentives from health insurance providers, can push patients to lead a healthier lifestyle while they save money.
- Opioid abuse prevention. Healthcare analysts can identify 742 risk factors that can accurately predict if someone is at risk for abusing opioids.
- Informed strategic planning. Big data offers researchers better insights into people’s motivations, which allows for effective strategic planning.
- Curing cancer by using health data. Cancer is a significant cause of death in today’s world. Still, medical researchers can use vast amounts of data regarding treatment plans and cancer patient recovery rates to find treatments, solutions, and trends with the highest success rates.
- Using predictive analysis. Predictive analysis can help doctors make data-driven decisions in seconds that can improve a patient’s treatment. New tools may help healthcare professionals predict risks for diabetes, therefore calling for more frequent screening and possible preventative measures.
- Enhancing data security. Data breaches are a serious and common problem, and a patient’s health records must always be kept secure. Analytics can identify changes in network traffic that indicate a possible hacker.
- Reducing fraud. By streamlining insurance claims processing, analytical tools can reduce inaccurate claims and insurance fraud.
- Practicing telemedicine. Not everyone has easy access to a healthcare facility. But today, thanks to the advent of smartphones, online video conferences, wireless devices, and wearables, clinicians can employ telemedicine to provide personalized treatment plans for remote patients and prevent hospitalization or re-admission.
- Integrating medical imaging. Each year, healthcare professionals in the United States perform almost 600 million imaging procedures. Big data analytics can change how images are read. For example, developed algorithms can analyze hundreds of thousands of images, identifying and quantifying specific pixel patterns to help physicians with their diagnoses.
- Preventing unnecessary emergency room visits. Healthcare analytics allows emergency rooms to easily share patient data, indicating whether the patient has already had specific tests done, a case manager at a different hospital or has been given medical advice.
- Learning and development. The better trained and more skilled your staff is, they will be more effective. This fact is significant when lives are at stake. By monitoring healthcare worker performance across the board while keeping track of their training data, institutions can use healthcare data analysis to get insights into who needs more training or support.
- Advanced risk and disease control. When a healthcare provider drills into insights like symptoms, medication type, and frequency of medical visits, among many others, it’s possible to provide better, more specialized preventative care, ultimately reducing hospital admissions.
- Self-harm and suicide prevention. Almost 800,000 people self-terminate every year worldwide, and about 17 percent of the world’s population will commit self-harm sometime during their lifetime. A combination of EHR data and a standard depression questionnaire can accurately identify people with an enhanced risk of a suicide attempt. Using a prediction algorithm, mental health professionals can flag patients likely to engage in these behaviors and get them the extra help they need.
- Better supply chain management. COVID showed us many flaws in how we do things, and one of the biggest lessons was the fragility of our supply chains. Institutions that leverage analytical tools to track supply chain performance metrics can make accurate, data-driven decisions regarding operations and spending, potentially saving hospitals millions of dollars per year.
- Financial facilities management. Healthcare institutions that invest in software to gather, arrange, and analyze their data can ensure their finances are solid and under control. Hence, the facility stays profitable while providing patients with the best care.
- Developing new therapies. By using a mix of historical, real-time, and predictive metrics in conjunction with a cohesive blend of data visualization techniques, healthcare experts can single out potential strengths and weaknesses in trials and processes.
- Managing and tracking epidemics, pandemics, and other instances of mass disease. By analyzing massive data sets pulled from diverse sources like medical records and individual human behavior, healthcare professionals can track how disease outbreaks are progressing in real-time.
- Improved drug prescription processes. Healthcare professionals can use data to predict critical scenarios, such as who’s likely to become addicted to particular medications or who isn’t sticking to their treatment. In the latter case, the company can immediately send personalized interventions and support efforts to ensure people adhere to their prescribed treatments.
- Preventing human error. People make mistakes; that’s why pencils have erasers. Healthcare institutions can leverage big data and forecasting to spot and quickly prevent fraud or human error.
- Using mobile devices to alert healthcare providers about coronary issues. Heart sensor data from a patient’s wearable tech (e.g., Apple watch) can detect irregular heartbeats and alert the patient’s physician.
- Using Bluetooth to help asthma patients. Bluetooth-powered inhalers can be connected to a patient’s smartphone to collect information regarding the frequency of their asthma attacks and the environmental conditions present during the attack.
How Data Analytics is Used to Advance Medical Research
Health data analytics provides medical researchers with valuable aid in advancing medical research in three fundamental ways:
- The analytics results help researchers identify approaches to improve clinical process efficiency and other healthcare operations.
- Cohort studies give medical researchers new insights into disease causes by successfully linking risk factors with health outcomes.
- Analytics-aided research personalizes healthcare provision, which results in more accurate diagnosis and treatment.
The Future of Data Analytics in Healthcare
The future looks bright for data analytics in all sectors, and healthcare is no exception. The latter’s importance has been made clear over the last few years due to the COVID-19 pandemic. Because of COVID, data analytics in healthcare has grown in importance. Healthcare organizations worldwide have turned to data analytics to manage the global health crisis and devise better treatments for patients, as hundreds of thousands worldwide have relied on a data analytics-fueled healthcare sector for COVID-19 treatments and prevention.
The need for quality healthcare remains constant, so data analytics in healthcare will always be relevant, and jobs in the field will remain in demand. Some sources state that although data analyst job demand is high in every industry, the most prominent role is that of the healthcare data analyst.
Going forward, experts project that healthcare analytics will have a significant impact in many other vital areas of the healthcare industry, including:
- Clinical trials . Health data analytics is expected to aid pharmaceutical researchers in going to market faster with essential new drugs.
- Epidemiology. Healthcare analytics professionals are experimenting with data visualization to identify better and more quickly control disease outbreaks in hopes of avoiding another COVID-like pandemic.
- Genomics . Health data analytics can advance understanding of how diseases affect different people. This understanding will enable medical researchers to develop personalized medicine tailored to individual DNA makeups.
- Social factors. The enhanced ability to analyze data about the social determinants of health (e.g., where patients live, work, and shop, what patients eat, environmental conditions, etc.) opens new possibilities to anticipate disease trends better and develop more effective health and disease prevention programs.
Positions in Data Analytics in Healthcare
If you search online for jobs in healthcare analytics, you will find available openings in related careers such as:
- Analytics manager
- Analytics specialist
- Associate director, research and analytics
- Senior analyst
- Senior healthcare analyst
- Strategic consultant
This small sample demonstrates the potential career paths you can take when you begin as a healthcare professional data analyst.
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Q: How is data analytics used in healthcare?
A: A partial list of uses of health data analytics includes:
- Improved predictions for patients
- Using Electronic Health Records (EHRs)
- Instant care real-time alerting
- Enhanced patient engagement
- Opioid abuse prevention
- Informed strategic planning
- Predictive analytics in healthcare
Q: What is the scope of big data analytics in healthcare?
A: Healthcare analytics relies on massive amounts of data, or as it’s commonly called, big data. Big data analytics in the future will increasingly focus on data freshness with the aim of better real-time analysis, enabling better-informed decisions. As big data continues to grow in scope, healthcare analytics will grow in tandem with it.
Q: What is predictive analytics in healthcare?
A: Predictive analytics is an advanced statistical technique that uses historical and real-time data to predict a particular outcome. By applying predictive analytics to consumer, patient, or claims data, healthcare professionals can forecast patterns or trends that can be leveraged to improve patient care and outreach initiatives. Leveraging insights from predictive models lets these healthcare professionals closely align their efforts with a preferred outcome. This alignment results in better decisions on investing budgets and resources and a better understanding of the patients they serve.