Healthcare is an inherently innovative industry, as the well-being and lives of people worldwide depend on how well the clinics adopt emerging tech. Big Data is one of the cross-industry disruptors that revolutionized how we collect, use, and store data, and healthcare is no exception. The use of Big Data in healthcare opens new possibilities for improving the quality of care, predicting treatment outcomes, defining the trends in public health, and more.
Big Data also offers new ways to optimize the clinic’s cost and protect the information using different data storage options (on-premise, cloud, or hybrid). However, the adoption of new tech has its drawbacks. Today, we will look at the benefits of Big Data, the challenges that stop more clinics from adopting it, and the most common use cases that prove its utility.
What Is Big Data in Healthcare, and Why Is It Important?
Big Data can be defined as high-volume, high-variety, and high-velocity information that requires efficient and innovative data processing to draw valuable insights from it. As the healthcare industry moves to digitize patient health records, it generates increasingly more information, which the typical database software tools cannot process. That is where Big Data comes into the picture offering much more efficient methods for processing information.
The health record is one of many sources of data in healthcare. Several actors contribute to data generation: telemedicine app users, governmental agencies and regulatory authorities, researchers, patient portals, and much more. That results in the exponential growth of processing information, which can only be ensured with the proper implementation of Big Data.
As the healthcare industry becomes increasingly digitized, it is evident that it will generate more and more data over time. The size of the global healthcare Big Data market will reach $81.3 billion by 2030 at a CAGR of 18.2%. Research facilities and hospitals worldwide produce significant amounts of unstructured clinical data daily. That pushes the need for adopting Big Data in healthcare even further.
What Are the Benefits of Data Analytics in Healthcare?
Big Data does not only help in processing information but also assists in drawing valuable insights and making better decisions, thanks to healthcare data analytics and predictive medicine. However, these are only some advantages of using Big Data in clinical environments.
Improved Patient Care
Treating patients, keeping them healthy, and preventing further health complications are the top priorities of any clinic. Understanding the trends in the patient’s condition, knowing their history, and making the right decisions to address their problems is vital for adequate care. Data analytics in healthcare helps physicians analyze all the factors contributing to the patient’s condition. That gives insight into the potential risks and the possible treatment to mitigate those risks and produce positive health outcomes.
You can also use data to educate patients, inform them and motivate them to take action to prevent potential complications. Gained insights can be implemented in telehealth applications so that users can access suggestions and information regarding their health straight from their smartphones without having to book the doctor’s office visit. That way, healthcare can become more accessible to the public and even lift some weight off the clinics during health crises like the ongoing pandemic.
Clinics often lose considerable money due to ineffective finance and resource management. The most common reason for that is staff overbooking or underbooking. Predictive analytics allows clinics to allocate human resources and schedule shifts more efficiently.
You can book more staff for the hottest shifts to cover all the patient needs or fewer staff members when there is a lower demand for clinical services. Data analytics tools will help you identify the patterns in the workload distribution throughout a given period. Thus, you will allocate your clinical staff more effectively.
Minimization of Human Error
Prescription and treatment errors have always been a severe problem for healthcare organizations. According to the Pharmacy Times, in the US alone, preventable medication errors cost up to $20.6 billion and affect around 7 million patients causing around 7,000 preventable deaths yearly. Data analytics in healthcare can help avoid wrong prescriptions by analyzing the patient’s health record, matching it with the prescribed treatment, and potentially flagging errors that may lead to undesirable or fatal consequences.
Healthcare data analytics help organizations make sense of health records and resource data and track and prevent security threats. Data analytics tools assist in identifying system functioning abnormalities, traffic changes, suspicious log-ins, and more. Cybersecurity is critical for Big Data apps for healthcare and industry in general.
As more patient data gets into the digital domain, it becomes increasingly important to pay attention to data security in healthcare. That is why a variety of international and local regulations, such as HIPAA compliance, define the standards for the lawful use and security of clinical data. Suppose you are building a telehealth application or any software that deals with patient data. In that case, you must comply with HIPAA standards and/or other regulations relevant to your region of operations.
What Are the Main Challenges to the Adoption of Big Data in Healthcare?
There are certain blockers to the universal spread of Big Data in healthcare, and understanding these blockers might shed some light on how to break through them.
The cost of Big Data adoption in healthcare still poses a considerable challenge for clinics across the US and the globe alike. Healthcare facilities must purchase the technology, hardware, and software tools to manage the data. More so, they need to either purchase Big Data apps for healthcare or commission the development of such apps on their terms.
The development of healthcare applications will cost a considerable sum, upward of hundreds of thousands of dollars, depending on the app’s specifications and complexity. Another item on the spending list is the data scientist’s salary, which can also be rather steep depending on where and how you hire.
That investment is necessary; if you want to use Big Data apps for healthcare within your organization, you must make it. Considering all the expenses that already hinder your clinic’s performance, this investment will be worth it. Work with a Big Data application development partner who can set up the development process concerning your budget. It will be possible to reduce the impact of the initial investment cost.
Data Aggregation and Cleaning
Healthcare data comes from multiple sources, from patient health records to clinical staff payrolls. Pulling all that data together and using it meaningfully requires collaboration between different actors. On top of that, Big Data is unstructured and heterogeneous. That calls for classification and aggregation techniques that make the data usable for further analysis.
The aggregated data will also require cleaning to ensure its accuracy and consistency. This process can be either automated or manual, of which prior is a preferable option for large batches of data. You will also need to deal with the new data in real-time. All the information must be cleansed and processed before it gets into storage. Otherwise, it may become damaged or spoiled, rendering it useless for further analytics and insights.
The use of healthcare data analytics is relatively new for the industry, and adopting Big Data calls for changes in the clinic’s culture. You will need to work with new experts, and the existing staff will require training to use the new tools effectively. Some clinics are forced to entirely replace their existing IT infrastructures, lay off certain employees, and adopt new operational practices. That causes stress and frustration for many people within clinics, but your organization must undergo that change.
The challenge is relevant for healthcare organizations cooperating with third parties outside their ecosystem. Many clinics still operate the old way using a pen-and-paper approach to health records. So, if you have transitioned to the new ways of gathering and storing patient data, do not be sure your peers from other clinics have done the same. When a patient has all their records stored on paper at another clinic, it might be hard for you to access their data and use your innovative data analytics and predictive medicine tools.
This problem will remain relevant for healthcare companies across the globe for years to come. Transitioning to new technology takes time, especially within developing communities. That is why technological discrepancies will continue to hinder the use of Big Data in healthcare in the future, albeit on a smaller level.
Use Cases of Big Data in Healthcare: How Doctors and Patients Benefit from It?
Now that we have established the benefits of Big Data in healthcare, what are its applications in the clinical environment? Here are the seven ways you can put Big Data to good use in your clinic. The use cases for Big Data do not end there, as there are many more ways to improve the quality of care using innovative tech.
Efficient and Accurate Diagnostics
The use of healthcare data analytics and Big Data helps with quick and accurate disease diagnostics. Traditionally, in case of a complicated ailment, the doctors would compare the patient’s symptoms to the disease histories they know and famous cases from medicine. Alternatively, they would research the literature and consult with colleagues discussing the symptoms and putting them within their knowledge framework.
Physicians can make this process much more efficient and faster using Big Data apps for healthcare. They can collect the data and feed it into the algorithm, and the app will suggest a list of likely diagnoses. The app can also suggest a list of tests that may be viable for narrowing down the potential diagnosis and cutting the unnecessary tests. All of that can boost the efficiency of an individual physician.
Modeling and Forecasting
Besides analyzing the current condition, Big Data is also a valuable tool for predictive medicine. Predictive modeling can derive insights from the current condition and predict the outcomes of disease and response to treatment. Other models can be used for modeling the development of a disease in a particular patient based on their habits, clinical records, family running diseases, and other health factors. For instance, predictive modeling has proven effective in identifying undiagnosed diabetes in patients displaying specific symptoms.
Public Health Monitoring
Data analytics helps in tracking public health trends. With that, you can analyze different cohorts of patients that exhibit common health patterns to predict possible risks in individual patients. Observing patients with similar symptoms helps define the typical progression of different diseases, which can be later used for clinical research and then extrapolated into actionable treatment procedures.
Telehealth apps rely heavily on Big Data since they generate high volumes of high-speed data. As both doctors and patients use telehealth applications, they generate enormous amounts of information that must be gathered, organized, and stored correctly for further use. When approaching a telehealth software development project, you must consider the sheer volume of data if you want your app to function properly.
Adopting Big Data apps for healthcare organizations can save countless lives thanks to preventive care. Predictive medicine can identify early signs of disease in patients and allow physicians to administer preventive care to avoid adverse outcomes. Physicians get an early reaction button that can help save lives before the health threat affects a patient.
Electronic Health Record (EHR)
Electronic health records are one of healthcare’s most significant data sources, and many clinics worldwide have already adopted or developed EHR platforms. Health records give doctors access to the patient’s medical history, which can be analyzed and interpreted via Big Data. Accessing the patient’s medical history, analyzing it, and drawing insights from it is crucial for administering proper care. With a complete picture, doctors can understand all the potential causes of a disease, which may lead to errors in the treatment process.
Hospital Resource Planning
Additionally, you can implement predictive analytics as a part of enterprise resource planning (ERP). Knowing what resources are used effectively and what is wasted can make all the difference for the cost-efficient functioning of the clinic. Even miscellaneous medical supplies like cotton pads or syringes can blow a hole in your budget if not managed properly. Planning your resources and knowing when to restock your supplies can save thousands upon thousands of dollars for your clinic each year.
Final Thoughts: Leverage Big Data with a Reliable Partner
The use of Big Data quickly becomes ubiquitous across healthcare facilities around the globe, though it still has a long way to go before becoming a universal practice. Cost optimization, decision support, minimization of human error, improved security, and, most importantly, increased quality and success of care can all be achieved via Big Data and data analytics.
Despite undeniable benefits, the successful adoption of Big Data still poses a tough challenge for many healthcare organizations. The primary obstacles here are the technical complexity and the cost of adoption. However, a reliable technical partnership can mitigate and deal with both. At SPsoft, we help partners address the primary challenges related to adopting Big Data in healthcare via tech expertise, domain knowledge, effective collaboration, and commitment to the highest standards of patient care. We can help you adopt Big Data fast and within your budget.