The rapidly expanding field of big data analytics have started to play a pivotal role in the evolution of healthcare practices and research. Due to increasing investments in workforce management tools, practice management solutions and electronic health record (EHRs) systems, the global big data analytics sector is projected to be worth a whopping $68 billion by 2024.
Big data analytics have been recently applied towards aiding the process of care delivery and disease exploration. It has also provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Big data analytics technologies are poised to enhance multiple areas of care - from medical imaging and chronic disease management to population health and precision medicine as these algorithms increase the efficiency of care delivery, reduce administrative burdens, and accelerate disease diagnosis.
Typically, big data refers to the abundant health data amassed from numerous ways of electronic health records - (EHRs), medical imaging, genomic sequencing, X-rays, and diagnostic reports, etc. Following are the benefits:
- Improves Patient healthcare: These cutting-edge analytics improves patient care in the healthcare system as this data facilitates doctors to prescribe effective treatment and make clinical decisions more accurate.
- Predicts patients at higher risk quickly and efficiently: Specific predictive analytics can categorize the patients who are at higher risk for diseases and hints for early intervention to protect them. This is especially helpful for people suffering from chronic diseases.
- Eases patient diagnostics with EHRs: EHRs, the most widespread application of big data enables effective patient diagnostics with every patient having their own electronic health records (EHRs).
- Ensures reduction of overall healthcare costs: Through predictive analytics, data helps to estimate individual patient costs and helps to maximize healthcare efficiency enormously by carefully planning the treatment.
- Generates real-time alerting: Certain medical healthcare decision support software analyzes medical data on the spot that deliver real-time alerting to help healthcare providers who in turn use that real-time data to deliver better prescriptive decisions.
- Enables improved healthcare with fitness devices: This analytical fitness products user data is analyzed which can be accessed by physicians to study the patients’ physical activity levels.
- Delivers greater insights into patient cohorts: Healthcare big data analysis draws a greater insight into patient cohorts that are at greatest risk for various illnesses, and in a way helps to take out some proactive prevention measures.
Some of the top challenges organizations typically face when booting up a big data analytics program and solutions to overcome them:
Challenges: Capturing data that is clean, complete, accurate, and formatted correctly for use in multiple systems is an ongoing battle for organizations.
Solution: Providers can improve the quality of their data by prioritizing valuable data types for their specific projects, enlisting the data governance and integrity expertise of health information management professionals, and developing clinical documentation improvement programs.
Challenge: Healthcare organizations lack clean data that ensures datasets are accurate, correct, consistent, relevant, and not corrupted in any way.
Solution: Choose IT vendors that offer automated scrubbing tools and use logic rules to compare, contrast, and correct large datasets.
Challenge: While many organizations deploy on premise data storage, that promise control over security, access, and up-time, an on-site server network can be expensive to scale, difficult to maintain, and prone to producing data siloes across different departments.
Solution: Many IT developers deploy hybrid infrastructure; providers should be careful to ensure that disparate systems are able to communicate and share data with other segments of the organization when necessary.
Challenge: Data security is the number one priority for healthcare organizations, especially in the wake of a rapid-fire series of high-profile breaches, hackings, and ransomware episodes.
Solution: Healthcare organizations must consistently review who has access to high-value data assets to prevent malicious parties from causing damage and remind their staff members of the critical nature of data security protocols.
Challenge: Developing complete, accurate, and up-to-date metadata is a key component of a successful data governance plan.
Solution: Healthcare organizations should assign a data steward to handle the development and curation of meaningful metadata.
Challenge: Robust metadata and strong stewardship protocols also make it easier for organizations to query their data and get the answers that they are expecting.
Solution: Though many organizations use Structured Query Language (SQL) to dive into large datasets and relational databases, its effectiveness depends on how the user trusts the accuracy, completeness, and standardization of the data at hand.
Challenge: After providers answer the query process, they must generate a clear, concise report that is easily accessible to the target audience.
Solution: Organizations should ensure clear cut plan as to how to use their reports to ensure that database administrators can generate the information they need.
Challenge: At the point of care, a clean and engaging data visualization can make it much easier for a clinician to absorb information and use it appropriately.
Solution: Common examples of data visualizations include heat maps, bar charts, pie charts, scatterplots, and histograms, all of which have their own specific uses to illustrate concepts and information.
Challenge: Healthcare data is not static, and most elements will require relatively frequent updates in order to remain current and relevant.
Solution: Organizations should ensure that they are not creating unnecessary duplicate records when attempting an update to a single element.
Data Sharing to External Sources
Challenge: Sharing data with external partners is essential, especially as the industry moves towards population health management and value-based care.
Solution: The industry is working hard to improve the sharing of data across technical and organizational barriers with emerging tools and strategies such as FHIR and public APIs.
The Future Forecast
With the influx of huge data, latest technologies including Big Data Analytics, Artificial Intelligence and Machine Learning are being increasingly used by healthcare organizations to gain real-time patient insights. Big data analytics empowers healthcare with actionable insights on patient’s data and outcomes and ensures to reduce overall healthcare costs, predict high risk patients more quickly, generate real-time alerting and so on.
A robust analytics-led strategy at the intersection of clinical, business, and technology perspectives has become imperative to help HCPs and associated firms meet the demands of patient care and accountability to various stakeholders simultaneously.
InfoVision helps HCPs and life sciences firms derive and offer true value through end-to-end digital transformation and analytics services. Our three-pronged approach to transforming healthcare and life sciences involves:
- An intelligent analytics-led core
- Design thinking-driven experiences and
- AI-driven automation
- We plug in intelligent insights and automation at various links of the value chain to help build 360⁰ patient profiles, democratize access, enhance operational efficiency and improve all-round patient outcomes.
- Healthcare Capabilities
- Strategy and Roadmap
- Data Engineering
- Operational Streamlining
- Remote Healthcare
- Insightful Patient Experience
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