Large and complex sets of data, collectively known as big data, have in the past few years changed the face of many industries, and the healthcare sector is one of them. Healthcare big data is introducing new possibilities of improving patient care, improving the results, and optimizing the processes. The healthcare field is also in a special place to apply big data to drive healthcare innovation due to the huge amount of data that is already being collected across various sources, including wearable devices, electronic health records (EHRs), and patient questionnaires. However, with these amazing possibilities come certain critical issues that require contemplation and study.
What is big data’s power in healthcare?
Big data in healthcare is the collection, review, and use of vast amounts of healthcare data to enhance patient outcomes, predict diseases, and reduce healthcare costs. We find this information in many different sources, such as the social determinants of health, genetics, imaging, and electronic health records. This data is being used because it is expected to gain insights that would revolutionize how healthcare is delivered; it is now possible to access information that was not previously possible.
The leading benefits of big data in healthcare include
- Personalized Care: By applying big data on the genetics, lifestyle, and history of patients, a healthcare practitioner can make the most effective treatments, and the process can be personalized to fulfill the requirements of every patient.
- Operational Efficiency: Big data can promote operations and ultimately cut costs and enrich patient experiences. These are streamlining of administrative functions and improvement of patient flow in hospitals.
Healthcare organizations can achieve the huge benefits of integrating data-driven strategies into clinical and operational processes when they do it successfully. Included in the numerous benefits of transforming data assets into data insights are better patient health, lower healthcare costs, more performance brought to light, and happier employees and customers. However, there are many obstacles to overcome and issues to resolve on the path to effective healthcare analytics.
Top challenges for big data in healthcare
Healthcare firms must carefully examine how they gather, store, analyze, and communicate their data to employees, business partners, and patients since big data is complicated and unmanageable.
1. Data privacy and security
Healthcare data is very sensitive, and so there are strict laws such as the HIPAA in the US and the GDPR in Europe. Data containing information about the patient is costly and hard to secure between mobile devices, cloud computing, and third-party technologies. Data breaches can severely destroy trust, and the process of regaining it may last years.
2. Lack of standardization and interoperability
Electronic medical records (EMRs), imaging, wearable technology, laboratories, and insurance systems are just a few of the many sources of healthcare data, which is frequently in walled systems and incompatible formats. Comprehensive analysis and data sharing are hampered by this fragmentation. Uneven adoption of standards like HL7 FHIR prevents institutions from integrating seamlessly.
3. Capture
Regretfully, it doesn’t always originate from a place with excellent data governance practices for a lot of healthcare providers. Organizations, many of which are not on the winning side of the struggle, are always fighting to capture data that is accurate, complete, clean, and structured correctly for use in numerous systems. In the era of electronic health records, artificial intelligence (AI), and machine learning (ML), a strong data-gathering procedure is essential to the advancement of big data analytics initiatives in the healthcare industry. One of the first things that businesses can do to support clinical care improvement efforts and create datasets is to properly record data.
4. Cleaning
Healthcare professionals may not be as conscious of the significance of cleaning their data as they are of the value of maintaining a clean clinic or operating room. Dirty data has the potential to bring down a large data analytics project in short order, especially when trying to visualize data across numerous, possibly overlapping sources, which may record operational or clinical dimensions in at least slightly different fashions.
5. Storage
For a healthcare IT department, data storage is a crucial performance, security, and cost concern. Some providers can no longer handle the expenses and effects of on-premise data centers due to the exponential growth in healthcare data volume. Although on-site server networks can be costly to scale, challenging to manage, and prone to creating data silos across departments, on-premise data storage offers control over security, access, and uptime. As costs decline and reliability increases, providers and payers are finding cloud storage and other digital health ecosystems more and more appealing. Although the cloud provides quick disaster recovery, reduced upfront expenses, and simpler expansion, businesses must be very cautious when selecting cloud storage providers that comply with the Health Insurance Portability and Accountability Act of 1996 (HIPAA).
6. Training and adoption barriers for healthcare professionals
Clinicians are not trained in data science; they are trained in care. Without adequate training, incorporating AI-powered dashboards, real-time monitoring, and alarms into healthcare procedures may cause conflict. Errors and underuse of data-driven tools might result from a lack of knowledge or an excessive dependence on technology.
7. Querying
Strong stewardship procedures and robust metadata also facilitate data querying and help businesses find the answers they need. The core of reporting and analytics is the ability to query data, but before healthcare organizations can analyze their big data assets in a meaningful way, they usually have to overcome some obstacles. First and foremost, they need to get over interoperability issues and data silos that restrict query tools from accessing the organization’s complete information repository. It might not be feasible to produce a comprehensive picture of the state of an organization or the health of a single patient if various dataset components are present in several walled-off systems or various formats.
8. Reporting
Clinicians who are attempting to use the information to treat patients may suffer if the process ends with dubious findings due to poor data collection at the beginning. Additionally, providers need to be aware of the distinction between “analysis” and “reporting.” Analysis frequently requires reporting as a precondition; the data must be extracted before it can be analyzed, but reporting can also be an independent final output.
9. Visualization
Clear and interesting data visualization can greatly facilitate a clinician’s ability to take in and apply information at the point of care. One of the typical data display techniques that tends to create an immediate response is color-coding, e.g., red, yellow, and green are widely recognized as stop, caution, and go. The businesses should also consider the best approaches to data presentation, such as the usage of charts to illustrate contrasting figures in the right ratio and the labeling of information to facilitate its clarity. Poor graphics, overcrowded or overlapping text, and complex flowcharts might annoy and frustrate the recipients so that they neglect or do not understand the contents.
End up
All these barriers will have to be addressed by the providers to establish a big-data interchange ecosystem that will connect all the stakeholders in the care continuum with accurate, timely, and actionable information. The outcome of success will reduce the burden of all those concerns, but it will need some time, effort, cash, and communication.
Furthermore, boost your career with Oxford Training Centre’s Healthcare Management Training Courses, designed to equip you with essential skills for effective healthcare leadership.
Enroll now in our healthcare management courses and become a certified professional in healthcare administration and management.