The current healthcare system has to cope with the increasing volumes of big data, as sources of such information multiply daily. They include medical records of patients, results of medical exams and training, medical research outcomes, etc.
The good thing about this trend is that digital transformation also conceived ways to handle the rising amounts of data. Furthermore, medical facilities utilize such essential information to benefit patients and the healthcare system.
Healthcare app developers significantly contribute to this process. Wireless devices, VR and 5G technologies, algorithms based on artificial intelligence (AI) require appropriate software designed to employ big data. Let’s review the use of big data in healthcare in detail.
Use Cases of Big Data that Improves Health Industry
For starters, big data in healthcare is large amounts of health and medicine-related information that can be used in advanced analytical programs.
With big data, medical specialists may improve their AI-driven programs based on machine learning (ML) techniques. It will reduce medical errors, model the spread of diseases, forecast pandemics, and much more.
Big data can be used in medicine anywhere: from primary and long-term care to health promotion and disease prevention.
Healthcare Management
In the table below, you can see how healthcare management has changed since the development of digital technologies.
| Obsolete methods before digital healthcare | Healthcare system had a paper-based medical records system. Communication was held in the oral form. No evidence of instructions or prescriptions given could be tracked. |
| Innovative methods with digital healthcare | Contemporary healthcare system has e-medical cards and e-prescriptions employed. Multiple software solutions enable reviewing employees’ performance, checking the availability of medications, tracking patients’ statuses, scheduling appointments, etc. |
Security
Healthcare facilities have to deal with medical insurance claims regularly. Such cases derive from accidents leading to the need for medical help. Malpractice cases are not unheard of as well. And corresponding insurance covers errors that happened during treatment delivery. In both situations, insurance companies are to verify a customer’s information when examining a claim.
Big data can increase the efficiency of case assessment processes by gathering and analyzing multiple factors that play a vital role in decision-making. It helps to prevent fraud and streamline payment processing to law-abiding patients.
Big data also helps assess risks for a specific accident with a particular person. Insurance companies offer different coverage plans based on such evaluations.
Data analytics and Forecasting
Big data in healthcare have multiple uses in advanced analytics. Some implementation methods are:
- Assistance in the analysis of research results
- Identification of problematic health areas for people in particular regions based on chronic disease statistics
- Forecasting potential pandemics
- Calculating readmission risks.
The number of reliable forecasting algorithms increases daily. Most likely, companies will employ more prediction methods shortly.
Development of Innovation
Big data greatly assists researchers in innovation development. Have you known that it may take up to 15 years to start the production of a new medication?
Companies save billions by letting big-data-based algorithms handle time-consuming analytics behind new therapies and meds development.
Telemedicine and Telehealth
Telemedicine is physicians’ practice of delivering care to their patients remotely. At the same time, telehealth is just a set of communication technologies needed to provide health services, no necessarily clinical ones, remotely.
For instance, big data-based ML techniques are widely incorporated to improve robot-assisted surgery. But they also greatly enhance initial diagnosing and virtual nursing processes overall.
Users of telehealth apps can be consulted anywhere and anytime. Physicians can monitor their patients’ state and wellbeing based on big data aided and generated statistical data being updated continuously.
Electronic Health Records (EHRs) Implementation
An Electronic Health Record (EHR) is a real-time updated patient-centered digital system. In other words, it is a personalized treatment history. Medical specialists involved in the healthcare delivery input all the relevant information.
EHRs are regarded as the biggest source of big data in healthcare. Data from records may be used to complement statistics and advanced analytics. But this information is private and cannot be shared without permission. Big data storages contain processed EHRs that do not indicate any personal details. They are typically used in cancer research areas.
A small piece of information received from one patient who remains unknown may save thousands of lives.
Challenges of Big Data Implementation in Healthcare
Rapid increase in the amount of big data and the number of its sources leads to the following limitations:
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Healthcare Cybersecurity and Information Privacy
Typically, large amounts of sensitive, personal information are stored in one database. Conventional digital storages are less likely to be subject to cybersecurity attacks. But big data storages are more vulnerable to security issues.
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Storage and Sharing of Health Data
Storing big data implies a strong need for sufficient database storage space, which is not always easy to arrange. Big data sharing is associated with the superior connection speed required, which may also be an issue.
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Data Standardization
Multiple sources of big data provide different formats of information. Data standardization ensures the correct use of data and enables further correlation. Still, it is challenging to standardize sets of tables and sequences of images, for example. All types of data content must be easily retrievable and compared.
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Lack of Data Governance
Unfortunately, there are still no effective governance procedures. There is a need for policies that would address issues of data formatting, standardizing, storing, and others. This should ensure consistent inspection and business rules in the health sector. Data governance may prevent data deterioration.
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Data Interpretation and Integration
Finally, there is a purely technical problem. It is associated with the difficulty of establishing well-functioning processes for big data integration. Synchronizing data sources and interpreting data to require skilled big data engineers.
The challenges for big data implementation in healthcare are substantial. But they do not reduce the pace of advanced technologies integration.
Why big data is an important part of digital health?
In essence, big data greatly benefit software, analytical methods, and AI-based algorithms in the digital healthcare systems.
Today, patients can receive error-free care and may even be diagnosed remotely. In their turn, medical specialists can use convenient digital electronic records systems.
Big-data-aided forecasting and ML-based analytics help both individual patients and entire populations. Finally, insurance companies may prevent fraud and promptly fund patients’ treatment.
