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Importance of big data in healthcare

Jagadeesan

4 mins

Importance of big data in healthcare

Healthcare providers are faced with innumerable challenges - adapting to a multifaceted market where medical costs have increased quickly while margins and reimbursements have fallen to new lows. These dramatic changes have created many hurdles for the delivery of proper care: rising regulatory requirements, healthcare reforms, lower reimbursements, and global competition, which leads to margin attrition. To remain competitive, healthcare companies are required to adopt a value-based approach to improve patient care while optimizing their processes to achieve higher efficiencies and reduce operating costs.

As a result of advancement in technologies, new opportunities have emerged. The collaboration model between healthcare providers, patients, and outsourcing partners have helped put a stronger focus on patient outcomes and new offerings in the ecosystem. Today’s real-time and data-driven approach reacts to healthcare industry changes faster than ever. In turn, healthcare companies must be equipped with the right technologies and resources to respond swiftly to inbound data and gain on the actionable insights to drive a responsive and patient-centric organization. 

Why is big data important in healthcare?

For Big Data to succeed in the healthcare industry, organizations must transform to overcome the challenges they face in an ever-changing technological environment.

Healthcare’s biggest data challenge is its historical siloed structure resulting from decade-old legacy systems. These disparate data silos prevent the access to complete patient profiles in real-time. It is essential going forward that organizations develop a data-centric approach allowing for the complete sharing of data across all channels – the right information at the right time in the right place.

Smart healthcare service implies making machines central to our healthcare condition by systematizing routine procedures and methods so clinicians can use an analytics platform to recognize critically sick patients and give actionable insights for care. Thus, clinicians will be able to more productively and viably analyze and treat patients. Smart healthcare service also implies computerizing billing, documentation, and administrative procedures so that clinicians can concentrate on addressing each patient’s needs.

Big data analytics platform also helps in predicting the rise in predominance in clinical trials each year due to the capacity of computers and machines to perform tasks generally requiring human thought. This new capacity should eventually enhance the quality, security, and time-to-market of rising treatments.

Benefits of big data in healthcare

Big data analytics is evolving into a promising field in healthcare. “Data Analytics” refers to the practice of collecting humongous amounts of unstructured, semi-structured and structured data and analyzing them to draw actionable insights and information contained therein, which assists healthcare organizations in informed decision making. This entire process is increasingly abetted by cutting-edge technologies that help to examine large volumes of data for hidden information patterns. 

Healthcare analysts use big data analytics to get data on health prevention, intervention and management. Following are few examples of the uses of big data in healthcare: 

Improve quality of care

The healthcare system is highly data reliant and data analytics can help healthcare organizations deliver on these demands and offer the system to sort through this rising concern of data complexity.

Harnessing the capabilities of big data analytics, organizations can create better time-to-value, make patients more accountable for their health conditions, improve outcomes and achieve effectiveness by maintaining small details to large processes in a healthcare network.

Data analytics can help in discovery, design programs, increase service operations, reduce risk, improve sustainability and assist health systems in efficient allocation of resources in order to maximize revenue, population health and most importantly- Patient Care.

Real-time monitoring

Moreover, hospitals can harness new technologies to analyze big volumes of generated data more proficiently to provide better coordinated patient care and clinical decision support by facilitating life-saving, real-time decisions at the point of care, especially in immediate need areas such as the emergency room.

In-time monitoring based on a continuous evaluation of patient health patterns through digitalized patient data enables physicians and other patient care personnel to make clinical decisions with a single view that draws together all the lab reports, medical tests and prescribed medications for a patient. Putting all together data from patient lifestyle choices and clinical files, this positively impacts patient health in the long term and improves quality of life.

Value-based care

Value and outcome-based initiatives boost performance improvements in the healthcare delivery system.  Accounting for costs is reciprocal to measuring the performance as well as valuing best practices. This means instead of focusing on reimbursement on a case-to-case basis, the overall outcomes determine payment.

Healthcare analytics can help identify hidden patterns in data that leads to better understanding of population health. A digitized system of interconnected Electronic Health Records (EHRs) or electronic health records available to physicians helps to provide all the detailed information that can help reduce costs by curtailing unnecessary care procedures. 

Furthermore, identification of repeated trends in population health using analytics can help forecast the individual patient costs; which in return helps health systems to allocate resources properly, reduce waste and maximize efficiency.

Predicting risks

One of the major costs to the healthcare industry is the treatment of chronic diseases. On a whole population level, analytics can help by predicting which patients are at a higher risk of getting infected and arrange primary interventions, before the problems develop. This includes collecting data related to a variety of factors including demographic and/or socio-economic profile, medical history, and comorbidities. Medical history involves blood pressure, age, blood glucose, cholesterol levels, and family history of chronic conditions. 

A bigger percentage of what marks health outcomes is linked with factors beyond the preview of traditional healthcare delivery. These factors include patient health habits, behaviors, socio-economic aspects like physical environments, employment and education.

For improving outcomes, the public health systems must expand their reach to tap into these measurable aspects. Using data analytics helps to predict the risk of attaining chronic diseases by modeling on these metrics.

Aggregating, analyzing and interpreting data from all these forms of information helps the healthcare industry to allocate the right resources at the right time, enabling it to belligerently intervene in populations at higher risks early-on and prevent systematic costs for a long term.

Conclusion

Healthcare industry is witnessing a paradigm shift from a volume-based care payment structure to a value-based payment structure for entities involved across value chain delivery. This puts focus on improved patient care, outcomes, usage of preventative medicines, while emplacing on building financially sustainable healthcare companies.

As healthcare companies differentiate from the competitive landscape by impacting patient outcomes positively, enhancing operational excellence, and customer service, patients benefit from this approach by actively participating in lifestyle choices that improve their health.

Physicians need to tap into data within their system in this complex healthcare industry to gain insights for providing a full spectrum of care. It is thus important to understand changes in uncertainty around regulations, reimbursement models, and curbing rising costs is instrumental in institutionalizing big data decision making.

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